diff --git a/eodag/resources/ext_product_types.json b/eodag/resources/ext_product_types.json index 6ac7cb659..b523fdc2b 100644 --- a/eodag/resources/ext_product_types.json +++ b/eodag/resources/ext_product_types.json @@ -1 +1 @@ -{"cop_marine": {"product_types_config": {"ANTARCTIC_OMI_SI_extent": {"abstract": "**DEFINITION**\n\nEstimates of Antarctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Southern Hemisphere (excluding ice lakes) and from 1993 up to real time aiming to:\ni) obtain the Antarctic sea ice extent as expressed in millions of km squared (106 km2) to monitor both the large-scale variability and mean state and change.\nii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss/gain since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013)). For the Southern Hemisphere, these trends are calculated from the annual mean values.\nThe Antarctic sea ice extent used here is based on the \u201cmulti-product\u201d (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread.\n\n**CONTEXT**\n\nSea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). \nThe sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018).\n \n**CMEMS KEY FINDINGS**\n\nWith quasi regular highs and lows, the annual Antarctic sea ice extent shows large variability until several monthly record high in 2014 and record lows in 2017, 2018 and 2023. Since the year 1993, the Southern Hemisphere annual sea ice extent regularly alternates positive and negative trend. The period 1993-2023 have seen a slight decrease at a rate of -0.28*106km2 per decade. This represents a loss amount of -2.4% per decade of Southern Hemisphere sea ice extent during this period; with however large uncertainties. The last quarter of the year 2016 and years 2017 and 2018 experienced unusual losses of ice. The year 2023 is an exceptional year and its average has a strong impact on the whole time series.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00186\n\n**References:**\n\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Samuelsen et al., 2016: Sea Ice In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9, 2016, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* Samuelsen et al., 2018: Sea Ice. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, 2018, DOI: 10.1080/1755876X.2018.1489208.\n* Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M.Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 317\u2013382, doi:10.1017/CBO9781107415324.012.\n", "doi": "10.48670/moi-00186", "instrument": null, "keywords": "antarctic-omi-si-extent,coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Sea Ice Extent from Reanalysis"}, "ANTARCTIC_OMI_SI_extent_obs": {"abstract": "**DEFINITION**\n\nSea Ice Extent (SIE) is defined as the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean surface that is covered with sea ice. SIC is computed from Passive Microwave satellite observations since 1979. \nSIE is often reported with units of 106 km2 (millions square kilometers). The change in sea ice extent (trend) is expressed in millions of km squared per decade (106 km2/decade). In addition, trends are expressed relative to the 1979-2022 period in % per decade.\nThese trends are calculated (i) from the annual mean values; (ii) from the September values (winter ice loss); (iii) from February values (summer ice loss). The annual mean trend is reported on the key figure, the September (maximum extent) and February (minimum extent) values are reported in the text below.\nSIE includes all sea ice, except for lake and river ice.\nSee also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI).\n\n**CONTEXT**\n\nSea ice is frozen seawater that floats at the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea-ice cover is changing is essential for monitoring the health of the Earth (Meredith et al. 2019). \n\n**CMEMS KEY FINDINGS**\n\nSince 1979, there has been an overall slight increase of sea ice extent in the Southern Hemisphere but a sharp decrease was observed after 2016. Over the period 1979-2022, the annual rate amounts to +0.02 +/- 0.05 106 km2 per decade (+0.18% per decade). Winter (September) sea ice extent trend amounts to +0.06 +/- 0.05106 km2 per decade (+0.32% per decade). Summer (February) sea ice extent trend amounts to -0.01+/- 0.05 106 km2 per decade (-0.38% per decade). These trend estimates are hardly significant, which is in agreement with the IPCC SROCC, which has assessed that \u2018Antarctic sea ice extent overall has had no statistically significant trend (1979\u20132018) due to contrasting regional signals and large interannual variability (high confidence).\u2019 (IPCC, 2019). Both June and July 2022 had the lowest average sea ice extent values for these months since 1979. \n\n**Figure caption**\n\na) The seasonal cycle of Southern Hemisphere sea ice extent expressed in millions of km2 averaged over the period 1979-2022 (red), shown together with the seasonal cycle in the year 2022 (green), and b) time series of yearly average Southern Hemisphere sea ice extent expressed in millions of km2. Time series are based on satellite observations (SMMR, SSM/I, SSMIS) by EUMETSAT OSI SAF Sea Ice Index (v2.2) with R&D input from ESA CCI. Details on the product are given in the corresponding PUM for this OMI. The change of sea ice extent over the period 1979-2022 is expressed as a trend in millions of square kilometers per decade and is plotted with a dashed line on panel b).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00187\n\n**References:**\n\n* Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* Samuelsen, A., L.-A. Breivik, R.P. Raj, G. Garric, L. Axell, E. Olason (2016): Sea Ice. In: The Copernicus Marine Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00187", "instrument": null, "keywords": "antarctic-omi-si-extent-obs,coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1978-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Monthly Sea Ice Extent from Observations Reprocessing"}, "ARCTIC_ANALYSISFORECAST_BGC_002_004": {"abstract": "The operational TOPAZ5-ECOSMO Arctic Ocean system uses the ECOSMO biological model coupled online to the TOPAZ5 physical model (ARCTIC_ANALYSISFORECAST_PHY_002_001 product). It is run daily to provide 10 days of forecast of 3D biogeochemical variables ocean. The coupling is done by the FABM framework.\n\nCoupling to a biological ocean model provides a description of the evolution of basic biogeochemical variables. The output consists of daily mean fields interpolated onto a standard grid and 40 fixed levels in NetCDF4 CF format. Variables include 3D fields of nutrients (nitrate, phosphate, silicate), phytoplankton and zooplankton biomass, oxygen, chlorophyll, primary productivity, carbon cycle variables (pH, dissolved inorganic carbon and surface partial CO2 pressure in seawater) and light attenuation coefficient. Surface Chlorophyll-a from satellite ocean colour is assimilated every week and projected downwards using a modified Ardyna et al. (2013) method. A new 10-day forecast is produced daily using the previous day's forecast and the most up-to-date prognostic forcing fields.\nOutput products have 6.25 km resolution at the North Pole (equivalent to 1/8 deg) on a stereographic projection. See the Product User Manual for the exact projection parameters.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00003\n\n**References:**\n\n* Ardyna, M., Babin, M., Gosselin, M., Devred, E., B\u00e9langer, S., Matsuoka, A., and Tremblay, J.-\u00c9.: Parameterization of vertical chlorophyll a in the Arctic Ocean: impact of the subsurface chlorophyll maximum on regional, seasonal, and annual primary production estimates, Biogeosciences, 10, 4383\u20134404, https://doi.org/10.5194/bg-10-4383-2013, 2013.\n* Yumruktepe, V. \u00c7., Samuelsen, A., and Daewel, U.: ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic, Geosci. Model Dev., 15, 3901\u20133921, https://doi.org/10.5194/gmd-15-3901-2022, 2022.\n", "doi": "10.48670/moi-00003", "instrument": null, "keywords": "arctic-analysisforecast-bgc-002-004,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,sea-water-ph-reported-on-total-scale,sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_002_001": {"abstract": "The operational TOPAZ5 Arctic Ocean system uses the HYCOM model and a 100-member EnKF assimilation scheme. It is run daily to provide 10 days of forecast (average of 10 members) of the 3D physical ocean, including sea ice with the CICEv5.1 model; data assimilation is performed weekly to provide 7 days of analysis (ensemble average).\n\nOutput products are interpolated on a grid of 6 km resolution at the North Pole on a polar stereographic projection. The geographical projection follows these proj4 library parameters: \n\nproj4 = \"+units=m +proj=stere +lon_0=-45 +lat_0=90 +k=1 +R=6378273 +no_defs\" \n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00001\n\n**References:**\n\n* Sakov, P., Counillon, F., Bertino, L., Lis\u00e6ter, K. A., Oke, P. R. and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8(4), 633\u2013656, doi:10.5194/os-8-633-2012, 2012.\n* Melsom, A., Counillon, F., LaCasce, J. H. and Bertino, L.: Forecasting search areas using ensemble ocean circulation modeling, Ocean Dyn., 62(8), 1245\u20131257, doi:10.1007/s10236-012-0561-5, 2012.\n", "doi": "10.48670/moi-00001", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-analysisforecast-phy-002-001,arctic-ocean,coastal-marine-environment,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": "proprietary", "missionStartDate": "2021-07-05T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Physics Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011": {"abstract": "The Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, including the Canadian Archipelago and the Bering Sea. neXtSIM is forced with surface atmosphere forcings from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ5, the ARC MFC PHY NRT system (002_001a). neXtSIM runs daily, assimilating manual ice charts, sea ice thickness from CS2SMOS in winter and providing 9-day forecasts. The output variables are the ice concentrations, ice thickness, ice drift velocity, snow depths, sea ice type, sea ice age, ridge volume fraction and albedo, provided at hourly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n\n**DOI (product):** \n\nhttps://doi.org/10.48670/moi-00004\n\n**References:**\n\n* Williams, T., Korosov, A., Rampal, P., and \u00d3lason, E.: Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F, The Cryosphere, 15, 3207\u20133227, https://doi.org/10.5194/tc-15-3207-2021, 2021.\n", "doi": "10.48670/moi-00004", "instrument": null, "keywords": "arctic-analysisforecast-phy-ice-002-011,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,surface-snow-thickness,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Sea Ice Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015": {"abstract": "The Arctic Ocean Surface Currents Analysis and Forecast system uses the HYCOM model at 3 km resolution forced with tides at its lateral boundaries, surface winds sea level pressure from the ECMWF (European Centre for Medium-Range Weather Forecasts) and wave terms (Stokes-Coriolis drift, stress and parameterisation of mixing by Langmuir cells) from the Arctic wave forecast. HYCOM runs daily providing 10 days forecast. The output variables are the surface currents and sea surface heights, provided at 15 minutes frequency, which therefore include mesoscale signals (though without data assimilation so far), tides and storm surge signals. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00005", "doi": "10.48670/moi-00005", "instrument": null, "keywords": "arctic-analysisforecast-phy-tide-002-015,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-surface-elevation,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Tidal Analysis and Forecast"}, "ARCTIC_ANALYSIS_FORECAST_WAV_002_014": {"abstract": "The Arctic Ocean Wave Analysis and Forecast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) together with tidal currents and ice from the ARC MFC forecasts (Sea Ice concentration and thickness). WAM runs twice daily providing one hourly 10 days forecast and one hourly 5 days forecast. From the output variables the most commonly used are significant wave height, peak period and mean direction.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00002", "doi": "10.48670/moi-00002", "instrument": null, "keywords": "arctic-analysis-forecast-wav-002-014,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Wave Analysis and Forecast"}, "ARCTIC_MULTIYEAR_BGC_002_005": {"abstract": "The TOPAZ-ECOSMO reanalysis system assimilates satellite chlorophyll observations and in situ nutrient profiles. The model uses the Hybrid Coordinate Ocean Model (HYCOM) coupled online to a sea ice model and the ECOSMO biogeochemical model. It uses the Determinstic version of the Ensemble Kalman Smoother to assimilate remotely sensed colour data and nutrient profiles. Data assimilation, including the 80-member ensemble production, is performed every 8-days. Atmospheric forcing fields from the ECMWF ERA-5 dataset are used\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00006\n\n**References:**\n\n* Simon, E., Samuelsen, A., Bertino, L. and Mouysset, S.: Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter, J. Mar. Syst., 152, 1\u201317, doi:10.1016/j.jmarsys.2015.07.004, 2015.\n", "doi": "10.48670/moi-00006", "instrument": null, "keywords": "arctic-multiyear-bgc-002-005,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2007-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "ARCTIC_MULTIYEAR_PHY_002_003": {"abstract": "The current version of the TOPAZ system - TOPAZ4b - is nearly identical to the real-time forecast system run at MET Norway. It uses a recent version of the Hybrid Coordinate Ocean Model (HYCOM) developed at University of Miami (Bleck 2002). HYCOM is coupled to a sea ice model; ice thermodynamics are described in Drange and Simonsen (1996) and the elastic-viscous-plastic rheology in Hunke and Dukowicz (1997). The model's native grid covers the Arctic and North Atlantic Oceans, has fairly homogeneous horizontal spacing (between 11 and 16 km). 50 hybrid layers are used in the vertical (z-isopycnal). TOPAZ4b uses the Deterministic version of the Ensemble Kalman filter (DEnKF; Sakov and Oke 2008) to assimilate remotely sensed as well as temperature and salinity profiles. The output is interpolated onto standard grids and depths for convenience. Daily values are provided at all depths and surfaces momentum and heat fluxes are provided as well. Data assimilation, including the 100-member ensemble production, is performed weekly.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00007", "doi": "10.48670/moi-00007", "instrument": null, "keywords": "arctic-multiyear-phy-002-003,arctic-ocean,coastal-marine-environment,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1991-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Physics Reanalysis"}, "ARCTIC_MULTIYEAR_PHY_ICE_002_016": {"abstract": "The Arctic Sea Ice Reanalysis system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, from Bering Strait to the North Atlantic. neXtSIM is forced by reanalyzed surface atmosphere forcings (ERA5) from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ4b, the ARC MFC MYP system (002_003). neXtSIM assimilates satellite sea ice concentrations from Passive Microwave satellite sensors, and sea ice thickness from CS2SMOS in winter from October 2010 onwards. The output variables are sea ice concentrations (total, young ice, and multi-year ice), sea ice thickness, sea ice velocity, snow depth on sea ice, sea ice type, sea ice age, sea ice ridge volume fraction and sea ice albedo, provided at daily and monthly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00336\n\n**References:**\n\n* Williams, T., Korosov, A., Rampal, P., and \u00d3lason, E.: Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F, The Cryosphere, 15, 3207\u20133227, https://doi.org/10.5194/tc-15-3207-2021, 2021.\n", "doi": "10.48670/mds-00336", "instrument": null, "keywords": "arctic-multiyear-phy-ice-002-016,arctic-ocean,coastal-marine-environment,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Sea Ice Reanalysis"}, "ARCTIC_MULTIYEAR_WAV_002_013": {"abstract": "The Arctic Ocean Wave Hindcast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA5 reanalysis together with ice from the ARC MFC reanalysis (Sea Ice concentration and thickness). Additionally, in the North Atlantic area, surface winds are used from a 2.5km atmospheric hindcast system. From the output variables the most commonly used are significant wave height, peak period and mean direction.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00008", "doi": "10.48670/moi-00008", "instrument": null, "keywords": "arctic-multiyear-wav-002-013,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-thickness,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Wave Hindcast"}, "ARCTIC_OMI_SI_Transport_NordicSeas": {"abstract": "**DEFINITION**\n\nNet sea-ice volume and area transport through the openings Fram Strait between Spitsbergen and Greenland along 79\u00b0N, 20\u00b0W - 10\u00b0E (positive southward); northern Barents Sea between Svalbard and Franz Josef Land archipelagos along 80\u00b0N, 27\u00b0E - 60\u00b0E (positive southward); eastern Barents Sea between the Novaya Zemlya and Franz Josef Land archipelagos along 60\u00b0E, 76\u00b0N - 80\u00b0N (positive westward). For further details, see Lien et al. (2021).\n\n**CONTEXT**\n\nThe Arctic Ocean contains a large amount of freshwater, and the freshwater export from the Arctic to the North Atlantic influence the stratification, and, the Atlantic Meridional Overturning Circulation (e.g., Aagaard et al., 1985). The Fram Strait represents the major gateway for freshwater transport from the Arctic Ocean, both as liquid freshwater and as sea ice (e.g., Vinje et al., 1998). The transport of sea ice through the Fram Strait is therefore important for the mass balance of the perennial sea-ice cover in the Arctic as it represents a large export of about 10% of the total sea ice volume every year (e.g., Rampal et al., 2011). Sea ice export through the Fram Strait has been found to explain a major part of the interannual variations in Arctic perennial sea ice volume changes (Ricker et al., 2018). The sea ice and associated freshwater transport to the Barents Sea has been suggested to be a driving mechanism for the presence of Arctic Water in the northern Barents Sea, and, hence, the presence of the Barents Sea Polar Front dividing the Barents Sea into a boreal and an Arctic part (Lind et al., 2018). In recent decades, the Arctic part of the Barents Sea has been giving way to an increasing boreal part, with large implications for the marine ecosystem and harvestable resources (e.g., Fossheim et al., 2015).\n\n**CMEMS KEY FINDINGS**\n\nThe sea-ice transport through the Fram Strait shows a distinct seasonal cycle in both sea ice area and volume transport, with a maximum in winter. There is a slight positive trend in the volume transport over the last two and a half decades. In the Barents Sea, a strong reduction of nearly 90% in average sea-ice thickness has diminished the sea-ice import from the Polar Basin (Lien et al., 2021). In both areas, the Fram Strait and the Barents Sea, the winds governed by the regional patterns of atmospheric pressure is an important driving force of temporal variations in sea-ice transport (e.g., Aaboe et al., 2021; Lien et al., 2021).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00192\n\n**References:**\n\n* Aaboe S, Lind S, Hendricks S, Down E, Lavergne T, Ricker R. 2021. Sea-ice and ocean conditions surprisingly normal in the Svalbard-Barents Sea region after large sea-ice inflows in 2019. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 140-148\n* Aagaard K, Swift JH, Carmack EC. 1985. Thermohaline circulation in the Arctic Mediterranean seas. J Geophys Res. 90(C7), 4833-4846\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Clim Change. doi:10.1038/nclimate2647\n* Lien VS, Raj RP, Chatterjee S. 2021. Modelled sea-ice volume and area transport from the Arctic Ocean to the Nordic and Barents seas. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 10-17\n* Lind S, Ingvaldsen RB, Furevik T. 2018. Arctic warming hotspot in the northern Barents Sea linked to declining sea ice import. Nature Clim Change. doi:10.1038/s41558-018-0205-y\n* Rampal P, Weiss J, Dubois C, Campin J-M. 2011. IPCC climate models do not capture Arctic sea ice drift acceleration: Consequences in terms of projected sea ice thinning and decline. J Geophys Res. 116, C00D07. https://doi.org/10.1029/2011JC007110\n* Ricker R, Girard-Ardhuin F, Krumpen T, Lique C. 2018. Satellite-derived sea ice export and its impact on Arctic ice mass balance. Cryosphere. 12, 3017-3032\n* Vinje T, Nordlund N, Kvambekk \u00c5. 1998. Monitoring ice thickness in Fram Strait. J Geophys Res. 103(C5), 10437-10449\n", "doi": "10.48670/moi-00192", "instrument": null, "keywords": "arctic-ocean,arctic-omi-si-transport-nordicseas,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Sea Ice Area/Volume Transport in the Nordic Seas from Reanalysis"}, "ARCTIC_OMI_SI_extent": {"abstract": "**DEFINITION**\n\nEstimates of Arctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Northern Hemisphere (excluding ice lakes) and from 1993 up to the year 2019 aiming to:\ni) obtain the Arctic sea ice extent as expressed in millions of km square (106 km2) to monitor both the large-scale variability and mean state and change.\nii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013). These trends are calculated in three ways, i.e. (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss).\nThe Arctic sea ice extent used here is based on the \u201cmulti-product\u201d (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread.\n\n**CONTEXT**\n\nSea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification, in global and regional sea level rates and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). \nThe sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 1993 to 2023 the Arctic sea ice extent has decreased significantly at an annual rate of -0.57*106 km2 per decade. This represents an amount of -4.8 % per decade of Arctic sea ice extent loss over the period 1993 to 2023. Over the period 1993 to 2018, summer (September) sea ice extent loss amounts to -1.18*106 km2/decade (September values), which corresponds to -14.85% per decade. Winter (March) sea ice extent loss amounts to -0.57*106 km2/decade, which corresponds to -3.42% per decade. These values slightly exceed the estimates given in the AR5 IPCC assessment report (estimate up to the year 2012) as a consequence of continuing Northern Hemisphere sea ice extent loss. Main change in the mean seasonal cycle is characterized by less and less presence of sea ice during summertime with time. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00190\n\n**References:**\n\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Samuelsen et al., 2016: Sea Ice In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9, 2016, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* Samuelsen et al., 2018: Sea Ice. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, 2018, DOI: 10.1080/1755876X.2018.1489208.\n* Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M.Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 317\u2013382, doi:10.1017/CBO9781107415324.012.\n", "doi": "10.48670/moi-00190", "instrument": null, "keywords": "arctic-ocean,arctic-omi-si-extent,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea Ice Extent from Reanalysis"}, "ARCTIC_OMI_SI_extent_obs": {"abstract": "**DEFINITION**\n\nSea Ice Extent (SIE) is defined as the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean that is covered with sea ice. It is computed from Passive Microwave satellite observations since 1979. \nSIE is often reported with units of 106 km2 (millions square kilometers). The change in sea ice extent (trend) is expressed in millions of km squared per decade (106 km2/decade). In addition, trends are expressed relative to the 1979-2022 period in % per decade.\nThese trends are calculated (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss). The annual mean trend is reported on the key figure, the March and September values are reported in the text below.\nSIE includes all sea ice, but not lake or river ice.\nSee also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI).\n\n**CONTEXT**\n\nSea ice is frozen seawater that floats at the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea ice cover is changing is essential for monitoring the health of the Earth. Sea ice has a significant impact on ecosystems and Arctic communities, as well as economic activities such as fisheries, tourism, and transport (Meredith et al. 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince 1979, the Northern Hemisphere sea ice extent has decreased at an annual rate of -0.51 +/- 0.03106 km2 per decade (-4.41% per decade). Loss of sea ice extent during summer exceeds the loss observed during winter periods: Summer (September) sea ice extent loss amounts to -0.81 +/- 0.06 106 km2 per decade (-12.73% per decade). Winter (March) sea ice extent loss amounts to -0.39 +/- 0.03 106 km2 per decade (-2.55% per decade). These values are in agreement with those assessed in the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (Meredith et al. 2019, with estimates up until year 2018). September 2022 had the 11th lowest mean September sea ice extent. Sea ice extent in September 2012 is to date the record minimum Northern Hemisphere sea ice extent value since the beginning of the satellite record, followed by September values in 2020.\n\n**Figure caption**\n\na) The seasonal cycle of Northern Hemisphere sea ice extent expressed in millions of km2 averaged over the period 1979-2022 (red), shown together with the seasonal cycle in the year 2022 (green), and b) time series of yearly average Northern Hemisphere sea ice extent expressed in millions of km2. Time series are based on satellite observations (SMMR, SSM/I, SSMIS) by EUMETSAT OSI SAF Sea Ice Index (v2.2) with R&D input from ESA CCI. Details on the product are given in the corresponding PUM for this OMI. The change of sea ice extent over the period 1979-2022 is expressed as a trend in millions of square kilometers per decade and is plotted with a dashed line in panel b).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00191\n\n**References:**\n\n* Samuelsen, A., L.-A. Breivik, R.P. Raj, G. Garric, L. Axell, E. Olason (2016): Sea Ice. In: The Copernicus Marine Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n", "doi": "10.48670/moi-00191", "instrument": null, "keywords": "arctic-ocean,arctic-omi-si-extent-obs,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1978-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Monthly Mean Sea Ice Extent from Observations Reprocessing"}, "ARCTIC_OMI_TEMPSAL_FWC": {"abstract": "**DEFINITION**\n\nEstimates of Arctic liquid Freshwater Content (FWC in meters) are obtained from integrated differences of the measured salinity and a reference salinity (set to 34.8) from the surface to the bottom per unit area in the Arctic region with a water depth greater than 500m as function of salinity (S), the vertical cell thickness of the dataset (dz) and the salinity reference (Sref). Waters saltier than the 34.8 reference are not included in the estimation. The regional FWC values from 1993 up to real time are then averaged aiming to:\n* obtain the mean FWC as expressed in cubic km (km3) \n* monitor the large-scale variability and change of liquid freshwater stored in the Arctic Ocean (i.e. the change of FWC in time).\n\n**CONTEXT**\n\nThe Arctic region is warming twice as fast as the global mean and its climate is undergoing unprecedented and drastic changes, affecting all the components of the Arctic system. Many of these changes affect the hydrological cycle. Monitoring the storage of freshwater in the Arctic region is essential for understanding the contemporary Earth system state and variability. Variations in Arctic freshwater can induce changes in ocean stratification. Exported southward downstream, these waters have potential future implications for global circulation and heat transport. \n\n**CMEMS KEY FINDINGS**\n\nSince 1993, the Arctic Ocean freshwater has experienced a significant increase of 423 \u00b1 39 km3/year. The year 2016 witnessed the highest freshwater content in the Artic since the last 24 years. Second half of 2016 and first half of 2017 show a substantial decrease of the FW storage. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00193\n\n**References:**\n\n* G. Garric, O. Hernandez, C. Bricaud, A. Storto, K. A. Peterson, H. Zuo, 2018: Arctic Ocean freshwater content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s70\u2013s72, DOI: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00193", "instrument": null, "keywords": "arctic-ocean,arctic-omi-tempsal-fwc,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Freshwater Content from Reanalysis"}, "BALTICSEA_ANALYSISFORECAST_BGC_003_007": {"abstract": "This Baltic Sea biogeochemical model product provides forecasts for the biogeochemical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Different datasets are provided. One with daily means and one with monthly means values for these parameters: nitrate, phosphate, chl-a, ammonium, dissolved oxygen, ph, phytoplankton, zooplankton, silicate, dissolved inorganic carbon, dissolved iron, dissolved cdom, hydrogen sulfide, and partial pressure of co2 at the surface. Instantaenous values for the Secchi Depth and light attenuation valid for noon (12Z) are included in the daily mean files/dataset. Additionally a third dataset with daily accumulated values of the netto primary production is available. The product is produced by the biogeochemical model ERGOM (Neumann et al, 2021) one way coupled to a Baltic Sea set up of the NEMO ocean model, which provides the Baltic Sea physical ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). This biogeochemical product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The product covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00009", "doi": "10.48670/moi-00009", "instrument": null, "keywords": "baltic-sea,balticsea-analysisforecast-bgc-003-007,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "BALTICSEA_ANALYSISFORECAST_PHY_003_006": {"abstract": "This Baltic Sea physical model product provides forecasts for the physical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Several datasets are provided: One with hourly instantaneous values, one with daily mean values and one with monthly mean values, all containing these parameters: sea level variations, ice concentration and thickness at the surface, and temperature, salinity and horizontal and vertical velocities for the 3D field. Additionally a dataset with 15 minutes (instantaneous) surface values are provided for the sea level variation and the surface horizontal currents, as well as detided daily values. The product is produced by a Baltic Sea set up of the NEMOv4.2.1 ocean model. This product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The ocean model is forced with Stokes drift data from the Baltic Sea Wave forecast product (BALTICSEA_ANALYSISFORECAST_WAV_003_010). Satellite SST, sea ice concentrations and in-situ T and S profiles are assimilated into the model's analysis field.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00010", "doi": "10.48670/moi-00010", "instrument": null, "keywords": "baltic-sea,balticsea-analysisforecast-phy-003-006,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Physics Analysis and Forecast"}, "BALTICSEA_ANALYSISFORECAST_WAV_003_010": {"abstract": "This Baltic Sea wave model product provides forecasts for the wave conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Data are provided with hourly instantaneous data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the Stokes drift, and two paramters for the maximum wave. The product is based on the wave model WAM cycle 4.7. The wave model is forced with surface currents, sea level anomaly and ice information from the Baltic Sea ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00011", "doi": "10.48670/moi-00011", "instrument": null, "keywords": "baltic-sea,balticsea-analysisforecast-wav-003-010,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-12-01T01:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Wave Analysis and Forecast"}, "BALTICSEA_MULTIYEAR_BGC_003_012": {"abstract": "This Baltic Sea Biogeochemical Reanalysis product provides a biogeochemical reanalysis for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the biogeochemical model ERGOM one-way online-coupled with the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include nitrate, phosphate, ammonium, dissolved oxygen, ph, chlorophyll-a, secchi depth, surface partial co2 pressure and net primary production. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n**DOI (product):**\n\nhttps://doi.org/10.48670/moi-00012", "doi": "10.48670/moi-00012", "instrument": null, "keywords": "baltic-sea,balticsea-multiyear-bgc-003-012,cell-thickness,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Biogeochemistry Reanalysis"}, "BALTICSEA_MULTIYEAR_PHY_003_011": {"abstract": "This Baltic Sea Physical Reanalysis product provides a reanalysis for the physical conditions for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include sea level, ice concentration, ice thickness, salinity, temperature, horizonal velocities and the mixed layer depths. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00013", "doi": "10.48670/moi-00013", "instrument": null, "keywords": "baltic-sea,balticsea-multiyear-phy-003-011,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Physics Reanalysis"}, "BALTICSEA_MULTIYEAR_WAV_003_015": {"abstract": "**This product has been archived** \n\n\n\nThis Baltic Sea wave model multiyear product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1980 and up to 0.5-1 year compared to real time.\nThis hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the maximum waves, and also the Stokes drift. Another dataset contains hourly values for five air-sea flux parameters. Additionally a dataset with monthly climatology are provided for the significant wave height and the wave period. The product is based on the wave model WAM cycle 4.7, and surface forcing from ECMWF's ERA5 reanalysis products. The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00014", "doi": "10.48670/moi-00014", "instrument": null, "keywords": "baltic-sea,balticsea-multiyear-wav-003-015,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,surface-roughness-length,wave-momentum-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T01:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Wave Hindcast"}, "BALTICSEA_REANALYSIS_WAV_003_015": {"abstract": "This Baltic Sea wave model hindcast product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1993 and up to 0.5-1 year compared to real time.\nThis hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, and also Stokes drift. Additionally a dataset with monthly climatology are provided for the significant wave height and the wave period. The product is based on the wave model WAM cycle 4.6.2, and surface forcing from ECMWF's ERA5 reanalysis products. The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00014", "doi": "10.48670/moi-00014", "instrument": null, "keywords": "baltic-sea,balticsea-reanalysis-wav-003-015,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T01:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "FMI (Finland)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Wave Hindcast"}, "BALTIC_OMI_HEALTH_codt_volume": {"abstract": "**DEFINITION**\n\nThe cod reproductive volume has been derived from regional reanalysis modelling results for the Baltic Sea BALTICSEA_MULTIYEAR_PHY_003_011 and BALTICSEA_MULTIYEAR_BGC_003_012. The volume has been calculated taking into account the three most important influencing abiotic factors of cod reproductive success: salinity > 11 g/kg, oxygen concentration\u2009>\u20092 ml/l and water temperature over 1.5\u00b0C (MacKenzie et al., 1996; Heikinheimo, 2008; Plikshs et al., 2015). The daily volumes are calculated as the volumes of the water with salinity > 11 g/kg, oxygen content\u2009>\u20092 ml/l and water temperature over 1.5\u00b0C in the Baltic Sea International Council for the Exploration of the Sea subdivisions of 25-28 (ICES, 2019).\n\n**CONTEXT**\n\nCod (Gadus morhua) is a characteristic fish species in the Baltic Sea with major economic importance. Spawning stock biomasses of the Baltic cod have gone through a steep decline in the late 1980s (Bryhn et al., 2022). Water salinity and oxygen concentration affect cod stock through the survival of eggs (Westin and Nissling, 1991; Wieland et al., 1994). Major Baltic Inflows provide a suitable environment for cod reproduction by bringing saline oxygenated water to the deep basins of the Baltic Sea (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland). Increased cod reproductive volume has a positive effect on cod reproduction success, which should reflect an increase of stock size indicator 4\u20135 years after the Major Baltic Inflow (Raudsepp et al., 2019). Eastern Baltic cod reaches maturity around age 2\u20133, depending on the population density and environmental conditions. Low oxygen and salinity cause stress, which negatively affects cod recruitment, whereas sufficient conditions may bring about male cod maturation even at the age of 1.5 years (Cardinale and Modin, 1999; Karasiova et al., 2008). There are a number of environmental factors affecting cod populations (Bryhn et al., 2022).\n\n**KEY FINDINGS**\n\nTypically, the cod reproductive volume in the Baltic Sea oscillates between 200 and 400 km\u00b3. There have been two distinct periods of significant increase, with maximum values reaching over 1200 km\u00b3, corresponding to the aftermath of Major Baltic Inflows (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland) from 2003 to 2004 and from 2016 to 2017. Following a decline to the baseline of 200 km\u00b3 in 2018, there was a rise to 800 km\u00b3 in 2019. The cod reproductive volume hit a second peak of 800 km\u00b3 in 2022 and has since stabilized at 600 km\u00b3. However, Bryhn et al. (2022) report no observed increase in the spawning stock biomass of the eastern Baltic Sea cod.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00196\n\n**References:**\n\n* Cardinale, M., Modin, J., 1999. Changes in size-at-maturity of Baltic cod (Gadus morhua) during a period of large variations in stock size and environmental conditions. Vol. 41 (3), 285-295. https://doi.org/10.1016/S0165-7836(99)00021-1\n* Heikinheimo, O., 2008. Average salinity as an index for environmental forcing on cod recruitment in the Baltic Sea. Boreal Environ Res 13:457\n* ICES, 2019. Baltic Sea Ecoregion \u2013 Fisheries overview, ICES Advice, DOI:10.17895/ices.advice.5566 Karasiova, E.M., Voss, R., Eero, M., 2008. Long-term dynamics in eastern Baltic cod spawning time: from small scale reversible changes to a recent drastic shift. ICES CM 2008/J:03\n* MacKenzie, B., St. John, M., Wieland, K., 1996. Eastern Baltic cod: perspectives from existing data on processes affecting growth and survival of eggs and larvae. Mar Ecol Prog Ser Vol. 134: 265-281.\n* Plikshs, M., Hinrichsen, H. H., Elferts, D., Sics, I., Kornilovs, G., K\u00f6ster, F., 2015. Reproduction of Baltic cod, Gadus morhua (Actinopterygii: Gadiformes: Gadidae), in the Gotland Basin: Causes of annual variability. Acta Ichtyologica et Piscatoria, Vol. 45, No. 3, 2015, p. 247-258.\n* Raudsepp, U., Legeais, J.-F., She, J., Maljutenko, I., Jandt, S., 2018. Baltic inflows. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s106\u2013s110, DOI: 10.1080/1755876X.2018.1489208\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, s26\u2013s30; DOI: 10.1080/ 1755876X.2019.1633075\n* Westin, L., Nissling, A., 1991. Effects of salinity on spermatozoa motility, percentage of fertilized eggs and egg development of Baltic cod Gadus morhua, and implications for cod stock fluctuations in the Baltic. Mar. Biol. 108, 5 \u2013 9.\n* Wieland, K., Waller, U., Schnack, D., 1994. Development of Baltic cod eggs at different levels of temperature and oxygen content. Dana 10, 163 \u2013 177.\n* Bryhn, A.C.., Bergek, S., Bergstr\u00f6m,U., Casini, M., Dahlgren, E., Ek, C., Hjelm, J., K\u00f6nigson, S., Ljungberg, P., Lundstr\u00f6m, K., Lunneryd, S.G., Oveg\u00e5rd, M., Sk\u00f6ld, M., Valentinsson, D., Vitale, F., Wennhage, H., 2022. Which factors can affect the productivity and dynamics of cod stocks in the Baltic Sea, Kattegat and Skagerrak? Ocean & Coastal Management, 223, 106154. https://doi.org/10.1016/j.ocecoaman.2022.106154\n", "doi": "10.48670/moi-00196", "instrument": null, "keywords": "baltic-omi-health-codt-volume,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-volume,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Cod Reproductive Volume from Reanalysis"}, "BALTIC_OMI_OHC_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe method for calculating the ocean heat content anomaly is based on the daily mean sea water potential temperature fields (Tp) derived from the Baltic Sea reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The total heat content is determined using the following formula:\n\nHC = \u03c1 * cp * ( Tp +273.15).\n\nHere, \u03c1 and cp represent spatially varying sea water density and specific heat, respectively, which are computed based on potential temperature, salinity and pressure using the UNESCO 1983 polynomial developed by Fofonoff and Millard (1983). The vertical integral is computed using the static cell vertical thicknesses sourced from the reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011 dataset cmems_mod_bal_phy_my_static, spanning from the sea surface to the 300 m depth. Spatial averaging is performed over the Baltic Sea spatial domain, defined as the region between 13\u00b0 - 31\u00b0 E and 53\u00b0 - 66\u00b0 N. To obtain the OHC annual anomaly time series in (J/m2), the mean heat content over the reference period of 1993-2014 was subtracted from the annual mean total heat content.\nWe evaluate the uncertainty from the mean annual error of the potential temperature compared to the observations from the Baltic Sea (Giorgetti et al., 2020). The shade corresponds to the RMSD of the annual mean heat content biases (\u00b1 35.3 MJ/m\u00b2) evaluated from the observed temperatures and corresponding model values. \nLinear trend (W/m2) has been estimated from the annual anomalies with the uncertainty of 1.96-times standard error.\n\n**CONTEXT**\n\nOcean heat content is a key ocean climate change indicator. It accounts for the energy absorbed and stored by oceans. Ocean Heat Content in the upper 2,000 m of the World Ocean has increased with the rate of 0.35 \u00b1 0.08 W/m2 in the period 1955\u20132019, while during the last decade of 2010\u20132019 the warming rate was 0.70 \u00b1 0.07 W/m2 (Garcia-Soto et al., 2021). The high variability in the local climate of the Baltic Sea region is attributed to the interplay between a temperate marine zone and a subarctic continental zone. Therefore, the Ocean Heat Content of the Baltic Sea could exhibit strong interannual variability and the trend could be less pronounced than in the ocean.\n\n**KEY FINDINGS**\n\nThe ocean heat content (OHC) of the Baltic Sea exhibits an increasing trend of 0.3\u00b10.1 W/m\u00b2, along with multi-year oscillations. This increase is less pronounced than the global OHC trend (Holland et al. 2019; von Schuckmann et al. 2019) and that of some other marginal seas (von Schuckmann et al. 2018; Lima et al. 2020). The relatively low trend values are attributed to the Baltic Sea's shallowness, which constrains heat accumulation in its waters. The most significant OHC anomaly was recorded in 2020, and following a decline from this peak, the anomaly has now stabilized at approximately 250 MJ/m\u00b2.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00322\n\n**References:**\n\n* Garcia-Soto C, Cheng L, Caesar L, Schmidtko S, Jewett EB, Cheripka A, Rigor I, Caballero A, Chiba S, B\u00e1ez JC, Zielinski T and Abraham JP (2021) An Overview of Ocean Climate Change Indicators: Sea Surface Temperature, Ocean Heat Content, Ocean pH, Dissolved Oxygen Concentration, Arctic Sea Ice Extent, Thickness and Volume, Sea Level and Strength of the AMOC (Atlantic Meridional Overturning Circulation). Front. Mar. Sci. 8:642372. doi: 10.3389/fmars.2021.642372\n* Fofonoff, P. and Millard, R.C. Jr UNESCO 1983. Algorithms for computation of fundamental properties of seawater. UNESCO Tech. Pap. in Mar. Sci., No. 44, 53 pp., p.39. http://unesdoc.unesco.org/images/0005/000598/059832eb.pdf\n* Giorgetti, A., Lipizer, M., Molina Jack, M.E., Holdsworth, N., Jensen, H.M., Buga, L., Sarbu, G., Iona, A., Gatti, J., Larsen, M. and Fyrberg, L., 2020. Aggregated and Validated Datasets for the European Seas: The Contribution of EMODnet Chemistry. Frontiers in Marine Science, 7, p.583657.\n* Holland E, von Schuckmann K, Monier M, Legeais J-F, Prado S, Sathyendranath S, Dupouy C. 2019. The use of Copernicus Marine Service products to describe the state of the tropical western Pacific Ocean around the islands: a case study. In: Copernicus Marine Service Ocean State Report, Issue 3. J Oper Oceanogr. 12(suppl. 1):s43\u2013s48. doi:10.1080/1755876X.2019.1633075\n* Lima L, Peneva E, Ciliberti S, Masina S, Lemieux B, Storto A, Chtirkova B. 2020. Ocean heat content in the Black Sea. In: Copernicus Marine Service Ocean State Report, Issue 4. J Oper Oceanogr. 13(suppl. 1):s41\u2013s48. doi:10.1080/1755876X.2020.1785097.\n* von Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G. 2019. Copernicus Marine Service Ocean State report. J Oper Oceanogr. 12(suppl. 1):s1\u2013s123. doi:10.1080/1755876X.2019.1633075.\n* von Schuckmann K, Storto A, Simoncelli S, Raj RP, Samuelsen A, Collar A, Sotillo MG, Szerkely T, Mayer M, Peterson KA, et al. 2018. Ocean heat content. In: Copernicus Marine Service Ocean State Report, issue 2. J Oper Oceanogr. 11 (Suppl. 1):s1\u2013s142. doi:10.1080/1755876X.2018.1489208.\n", "doi": "10.48670/mds-00322", "instrument": null, "keywords": "baltic-omi-ohc-area-averaged-anomalies,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,ohc-balrean,sea-water-salinity,sea-water-temperature,volume-fraction-of-oxygen-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ocean Heat Content Anomaly (0-300m) from Reanalysis"}, "BALTIC_OMI_SI_extent": {"abstract": "**DEFINITION**\n\nSea ice extent is defined as the area covered by sea ice, that is the area of the ocean having more than 15% sea ice concentration. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. Daily sea ice extent values are computed from the daily sea ice concentration maps. All sea ice covering the Baltic Sea is included, except for lake ice. The data used to produce the charts are Synthetic Aperture Radar images as well as in situ observations from ice breakers (Uiboupin et al., 2010). The annual course of the sea ice extent has been calculated as daily mean ice extent for each day-of-year over the period October 1992 \u2013 September 2014. Weekly smoothed time series of the sea ice extent have been calculated from daily values using a 7-day moving average filter.\n\n**CONTEXT**\n\nSea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Moreover, it is an important parameter for safe winter navigation. The presence of sea ice cover sets special requirements for navigation, both for the construction of the ships and their behavior in ice, as in many cases, merchant ships need icebreaker assistance. Temporal trends of the sea ice extent could be a valuable indicator of the climate change signal in the Baltic Sea region. It has been estimated that a 1 \u00b0C increase in the average air temperature results in the retreat of ice-covered area in the Baltic Sea about 45,000 km2 (Granskog et al., 2006). Decrease in maximum ice extent may influence vertical stratification of the Baltic Sea (Hordoir and Meier, 2012) and affect the onset of the spring bloom (Eilola et al., 2013). In addition, statistical sea ice coverage information is crucial for planning of coastal and offshore construction. Therefore, the knowledge about ice conditions and their variability is required and monitored in Copernicus Marine Service.\n\n**KEY FINDINGS**\n\nSea ice in the Baltic Sea exhibits a strong seasonal pattern. Typically, formation begins in October and can persist until June. The 2022/23 ice season saw a relatively low maximum ice extent in the Baltic Sea, peaking at around 65,000 km\u00b2. Formation started in November and accelerated in the second week of December. The ice extent then remained fairly stable and below the climatological average until the end of January. From February to the second week of March, the extent of sea ice steadily grew to its maximum of 65,000 km\u00b2, before gradually receding. The peak day for sea ice extent varies annually but generally oscillates between the end of February and the start of March (Singh et al., 2024). The past 30 years saw the highest sea ice extent at 260,000 km\u00b2 in 2010/11. Despite a downward trend in sea ice extent in the Baltic Sea from 1993 to 2022, the linear trend does not show statistical significance.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00200\n\n**References:**\n\n* Eilola K, M\u00e5rtensson S, Meier HEM, 2013. Modeling the impact of reduced sea ice cover in future climate on the Baltic Sea biogeochemistry. Geophysical Research Letters, 40, 149-154, doi:10.1029/2012GL054375\n* Granskog M, Kaartokallio H, Kuosa H, Thomas DN, Vainio J, 2006. Sea ice in the Baltic Sea \u2013 A review. Estuarine, Coastal and Shelf Science, 70, 145\u2013160. doi:10.1016/j.ecss.2006.06.001\n* Hordoir R., Meier HEM, 2012. Effect of climate change on the thermal stratification of the Baltic Sea: a sensitivity experiment. Climate Dynamics, 38, 1703-1713, doi:10.1007/s00382-011-1036-y\n* Uiboupin R, Axell L, Raudsepp U, Sipelgas L, 2010. Comparison of operational ice charts with satellite based ice concentration products in the Baltic Sea. 2010 IEEE/ OES US/EU Balt Int Symp Balt 2010, doi:10.1109/BALTIC.2010.5621649\n* Vihma T, Haapala J, 2009. Geophysics of sea ice in the Baltic Sea: A review. Progress in Oceanography, 80, 129-148, doi:10.1016/j.pocean.2009.02.002\n", "doi": "10.48670/moi-00200", "instrument": null, "keywords": "baltic-omi-si-extent,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ice Extent from Observations Reprocessing"}, "BALTIC_OMI_SI_volume": {"abstract": "**DEFINITION**\n\nThe sea ice volume is a product of sea ice concentration and sea ice thickness integrated over respective area. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. The Baltic Sea area having more than 15% of sea ice concentration is included into the sea ice volume analysis. Daily sea ice volume values are computed from the daily sea ice concentration and sea ice thickness maps. The data used to produce the charts are Synthetic Aperture Radar images as well as in situ observations from ice breakers (Uiboupin et al., 2010; https://www.smhi.se/data/oceanografi/havsis). The annual course of the sea ice volume has been calculated as daily mean ice volume for each day-of-year over the period October 1992 \u2013 September 2014. Weekly smoothed time series of the sea ice volume have been calculated from daily values using a 7-day moving average filter.\n\n**CONTEXT**\n\nSea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Knowledge of the sea ice volume facilitates planning of icebreaking activity and operation of the icebreakers (Valdez Banda et al., 2015; Bostr\u00f6m and \u00d6sterman, 2017). A long-term monitoring of ice parameters is required for design and installation of offshore constructions in seasonally ice covered seas (Heinonen and Rissanen, 2017). A reduction of the sea ice volume in the Baltic Sea has a critical impact on the population of ringed seals (Harkonen et al., 2008). Ringed seals need stable ice conditions for about two months for breeding and moulting (Sundqvist et al., 2012). The sea ice is a habitat for diverse biological assemblages (Enberg et al., 2018).\n\n**KEY FINDINGS**\n\nIn the Baltic Sea, the ice season may begin in October and last until June. The maximum sea ice volume typically peaks in March, but in 2023, it was observed in April. The 2022/23 ice season saw a low maximum sea ice volume of approximately 14 km\u00b3. From 1993 to 2023, the annual maximum ice volume ranged from 4 km\u00b3 in 2020 to 60 km\u00b3 in 1996. There is a statistically significant downward trend of -0.73 km\u00b3/year (p=0.01) in the Baltic Sea's maximum sea ice volume. Recent trends indicate a decrease in sea ice fraction and thickness across the Baltic Sea, particularly in the Bothnian Bay, as reported by Singh et al. (2024).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00201\n\n**References:**\n\n* Bostr\u00f6m M, \u00d6sterman C, 2017, Improving operational safety during icebreaker operations, WMU Journal of Maritime Affairs, 16, 73-88, DOI: 10.1007/s13437-016-0105-9\n* Enberg S, Majaneva M, Autio R, Blomster J, Rintala J-M, 2018, Phases of microalgal succession in sea ice and the water column in the baltic sea from autumn to spring. Marine Ecology Progress Series, 559, 19-34. DOI: 10.3354/meps12645\n* Harkonen T, J\u00fcssi M, J\u00fcssi I, Verevkin M, Dmitrieva L, Helle E, Sagitov R, Harding KC, 2008, Seasonal Activity Budget of Adult Baltic Ringed Seals, PLoS ONE 3(4): e2006, DOI: 0.1371/journal.pone.0002006\n* Heinonen J, Rissanen S, 2017, Coupled-crushing analysis of a sea ice - wind turbine interaction \u2013 feasibility study of FAST simulation software, Ships and Offshore Structures, 12, 1056-1063. DOI: 10.1080/17445302.2017.1308782\n* Sundqvist L, Harkonen T, Svensson CJ, Harding KC, 2012, Linking Climate Trends to Population Dynamics in the Baltic Ringed Seal: Impacts of Historical and Future Winter Temperatures, AMBIO, 41: 865, DOI: 10.1007/s13280-012-0334-x\n* Uiboupin R, Axell L, Raudsepp U, Sipelgas L, 2010, Comparison of operational ice charts with satellite based ice concentration products in the Baltic Sea. 2010 IEEE/ OES US/EU Balt Int Symp Balt 2010, DOI: 10.1109/BALTIC.2010.5621649\n* Valdez Banda OA, Goerlandt F, Montewka J, Kujala P, 2015, A risk analysis of winter navigation in Finnish sea areas, Accident Analysis & Prevention, 79, 100\u2013116, DOI: 10.1016/j.aap.2015.03.024\n", "doi": "10.48670/moi-00201", "instrument": null, "keywords": "baltic-omi-si-volume,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-volume,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ice Volume from Observations Reprocessing"}, "BALTIC_OMI_TEMPSAL_Stz_trend": {"abstract": "**DEFINITION**\n\nThe subsurface salinity trends have been derived from regional reanalysis and forecast modelling results of the Copernicus Marine Service BAL MFC group for the Baltic Sea (product reference BALTICSEA_MULTIYEAR_PHY_003_011). The salinity trend has been obtained through a linear fit for each time series of horizontally averaged (13 \u00b0E - 31 \u00b0E and 53 \u00b0N - 66 \u00b0N; excluding the Skagerrak strait) annual salinity and at each depth level.\n\n**CONTEXT**\n\nThe Baltic Sea is a brackish semi-enclosed sea in North-Eastern Europe. The surface salinity varies horizontally from ~10 near the Danish Straits down to ~2 at the northernmost and easternmost sub-basins of the Baltic Sea. The halocline, a vertical layer with rapid changes of salinity with depth that separates the well-mixed surface layer from the weakly stratified layer below, is located at the depth range of 60-80 metres (Matth\u00e4us, 1984). The bottom layer salinity below the halocline depth varies from 15 in the south down to 3 in the northern Baltic Sea (V\u00e4li et al., 2013). The long-term salinity is determined by net precipitation and river discharge as well as saline water inflows from the North Sea (Lehmann et al., 2022). Long-term salinity decrease may reduce the occurrence and biomass of the Fucus vesiculosus - Idotea balthica association/symbiotic aggregations (Kotta et al., 2019). Changes in salinity and oxygen content affect the survival of the Baltic cod eggs (Raudsepp et al, 2019; von Dewitz et al., 2018).\n\n**KEY FINDINGS**\n\nThe subsurface salinity from 1993 to 2023 exhibits distinct variations at different depths. In the surface layer up to 25 meters, which aligns with the average upper mixed layer depth in the Baltic Sea, there is no discernible trend. The salinity trend increases steadily from zero at a 25-meter depth to 0.04 per year at 70 meters. The most pronounced trend, 0.045 per year, is found within the extended halocline layer ranging from 70 to 150 meters. It is noteworthy that there is a slight reduction in the salinity trend to 0.04 per year between depths of 150 and 220 meters. Although this decrease is minor, it suggests that salt transport into the extended halocline layer is more pronounced than into the deeper layers. The Major Baltic Inflows are responsible for the significant salinity trend of 0.05 per year observed in the deepest layer of the Baltic Sea. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00207\n\n**References:**\n\n* von Dewitz B, Tamm S, Ho\u00c8flich K, Voss R, Hinrichsen H-H., 2018. Use of existing hydrographic infrastructure to forecast the environmental spawning conditions for Eastern Baltic cod, PLoS ONE 13(5): e0196477, doi:10.1371/journal.pone.0196477\n* Kotta, J., Vanhatalo, J., J\u00e4nes, H., Orav-Kotta, H., Rugiu, L., Jormalainen, V., Bobsien, I., Viitasalo, M., Virtanen, E., Nystr\u00f6m Sandman, A., Isaeus, M., Leidenberger, S., Jonsson, P.R., Johannesson, K., 2019. Integrating experimental and distribution data to predict future species patterns. Scientific Reports, 9: 1821, doi:10.1038/s41598-018-38416-3\n* Matth\u00e4us W, 1984, Climatic and seasonal variability of oceanological parameters in the Baltic Sea, Beitr. Meereskund, 51, 29\u201349.\n* Sandrine Mulet, Bruno Buongiorno Nardelli, Simon Good, Andrea Pisano, Eric Greiner, Maeva Monier, Emmanuelle Autret, Lars Axell, Fredrik Boberg, Stefania Ciliberti, Marie Dr\u00e9villon, Riccardo Droghei, Owen Embury, J\u00e9rome Gourrion, Jacob H\u00f8yer, M\u00e9lanie Juza, John Kennedy, Benedicte Lemieux-Dudon, Elisaveta Peneva, Rebecca Reid, Simona Simoncelli, Andrea Storto, Jonathan Tinker, Karina von Schuckmann and Sarah L. Wakelin. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI:10.1080/1755876X.2018.1489208\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. 2.7 Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3\n* V\u00e4li G, Meier HEM, Elken J, 2013, Simulated halocline variability in the baltic sea and its impact on hypoxia during 1961-2007, Journal of Geophysical Research: Oceans, 118(12), 6982\u20137000, DOI:10.1002/2013JC009192\n", "doi": "10.48670/moi-00207", "instrument": null, "keywords": "baltic-omi-tempsal-stz-trend,baltic-sea,coastal-marine-environment,confidence-interval,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-salinity-trend,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Subsurface Salinity trend from Reanalysis"}, "BALTIC_OMI_TEMPSAL_Ttz_trend": {"abstract": "**DEFINITION**\n\nThe subsurface temperature trends have been derived from regional reanalysis results for the Baltic Sea (product references BALTICSEA_MULTIYEAR_PHY_003_011). Horizontal averaging has been done over the Baltic Sea domain (13 \u00b0E - 31 \u00b0E and 53 \u00b0N - 66 \u00b0N; excluding the Skagerrak strait). The temperature trend has been obtained through a linear fit for each time series of horizontally averaged annual temperature and at each depth level. \n\n**CONTEXT**\n\nThe Baltic Sea is a semi-enclosed sea in North-Eastern Europe. The temperature of the upper mixed layer of the Baltic Sea is characterised by a strong seasonal cycle driven by the annual course of solar radiation (Lepp\u00e4ranta and Myrberg, 2008). The maximum water temperatures in the upper layer are reached in July and August and the minimum during February, when the Baltic Sea becomes partially frozen (CMEMS OMI Baltic Sea Sea Ice Extent, CMEMS OMI Baltic Sea Sea Ice Volume). Seasonal thermocline, developing in the depth range of 10-30 m in spring, reaches its maximum strength in summer and is eroded in autumn. During autumn and winter the Baltic Sea is thermally mixed down to the permanent halocline in the depth range of 60-80 metres (Matth\u00e4us, 1984). The 20\u201350\u202fm thick cold intermediate layer forms below the upper mixed layer in March and is observed until October within the 15-65 m depth range (Chubarenko and Stepanova, 2018; Liblik and Lips, 2011). The deep layers of the Baltic Sea are disconnected from the ventilated upper ocean layers, and temperature variations are predominantly driven by mixing processes and horizontal advection. A warming trend of the sea surface waters is positively correlated with the increasing trend of diffuse attenuation of light (Kd490) and satellite-detected chlorophyll concentration (Kahru et al., 2016). Temperature increase in the water column could accelerate oxygen consumption during organic matter oxidation (Savchuk, 2018).\n\n**KEY FINDINGS**\n\nAnalysis of subsurface temperatures from 1993 to 2023 indicates that the Baltic Sea is experiencing warming across all depth intervals. The temperature trend in the upper mixed layer (0-25 m) is approximately 0.055 \u00b0C/year, decreasing to 0.045 \u00b0C/year within the seasonal thermocline layer. A peak temperature trend of 0.065 \u00b0C/year is observed at a depth of 70 m, aligning with the base of the cold intermediate layer. Beyond this depth, the trend stabilizes, closely matching the 0.065 \u00b0C/year value. At a 95% confidence level, it can be stated that the Baltic Sea's warming is consistent with depth, averaging around 0.06 \u00b0C/year. Notably, recent trends show a significant increase; for instance, Savchuk's 2018 measurements indicate an average temperature trend of 0.04 \u00b0C/year in the Baltic Proper's deep layers (>60m) from 1979 to 2016.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00208\n\n**References:**\n\n* Chubarenko, I., Stepanova, N. 2018. Cold intermediate layer of the Baltic Sea: Hypothesis of the formation of its core. Progress in Oceanography, 167, 1-10, doi: 10.1016/j.pocean.2018.06.012\n* Kahru, M., Elmgren, R., and Savchuk, O. P. 2016. Changing seasonality of the Baltic Sea. Biogeosciences 13, 1009\u20131018. doi: 10.5194/bg-13-1009-2016\n* Lepp\u00e4ranta, M., Myrberg, K. 2008. Physical Oceanography of the Baltic Sea. Springer, Praxis Publishing, Chichester, UK, pp. 370\n* Liblik, T., Lips, U. 2011. Characteristics and variability of the vertical thermohaline structure in the Gulf of Finland in summer. Boreal Environment Research, 16, 73-83.\n* Matth\u00e4us W, 1984, Climatic and seasonal variability of oceanological parameters in the Baltic Sea, Beitr. Meereskund, 51, 29\u201349.\n* Savchuk, .P. 2018. Large-Scale Nutrient Dynamics in the Baltic Sea, 1970\u20132016. Frontiers in Marine Science, 5:95, doi: 10.3389/fmars.2018.00095\n", "doi": "10.48670/moi-00208", "instrument": null, "keywords": "baltic-omi-tempsal-ttz-trend,baltic-sea,coastal-marine-environment,confidence-interval,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-temperature-trend,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Subsurface Temperature trend from Reanalysis"}, "BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm": {"abstract": "**DEFINITION**\n\nMajor Baltic Inflows bring large volumes of saline and oxygen-rich water into the bottom layers of the deep basins of the Baltic Sea- Bornholm basin, Gdansk basin and Gotland basin. The Major Baltic Inflows occur seldom, sometimes many years apart (Mohrholz, 2018). The Major Baltic Inflow OMI consists of the time series of the bottom layer salinity in the Arkona basin and in the Bornholm basin and the time-depth plot of temperature, salinity and dissolved oxygen concentration in the Gotland basin (BALTIC_OMI_WMHE_mbi_sto2tz_gotland). Bottom salinity increase in the Arkona basin is the first indication of the saline water inflow, but not necessarily Major Baltic Inflow. Abrupt increase of bottom salinity of 2-3 units in the more downstream Bornholm basin is a solid indicator that Major Baltic Inflow has occurred.\nThe subsurface temperature trends have been derived from regional reanalysis results for the Baltic \n\n**CONTEXT**\n\nThe Baltic Sea is a huge brackish water basin in Northern Europe whose salinity is controlled by its freshwater budget and by the water exchange with the North Sea (e.g. Neumann et al., 2017). The saline and oxygenated water inflows to the Baltic Sea through the Danish straits, especially the Major Baltic Inflows, occur only intermittently (e.g. Mohrholz, 2018). Long-lasting periods of oxygen depletion in the deep layers of the central Baltic Sea accompanied by a salinity decline and the overall weakening of vertical stratification are referred to as stagnation periods. Extensive stagnation periods occurred in the 1920s/1930s, in the 1950s/1960s and in the 1980s/beginning of 1990s Lehmann et al., 2022). Bottom salinity variations in the Arkona Basin represent water exchange between the Baltic Sea and Skagerrak-Kattegat area. The increasing salinity signal in that area does not indicate that a Major Baltic Inflow has occurred. The mean sea level of the Baltic Sea derived from satellite altimetry data can be used as a proxy for the detection of saline water inflows to the Baltic Sea from the North Sea (Raudsepp et al., 2018). The medium and strong inflow events increase oxygen concentration in the near-bottom layer of the Bornholm Basin while some medium size inflows have no impact on deep water salinity (Mohrholz, 2018). \n\n**KEY FINDINGS**\n\nTime series data of bottom salinity variations in the Arkona Basin are instrumental for monitoring the sporadic nature of water inflow and outflow events. The bottom salinity in the Arkona Basin fluctuates between 11 and 25 g/kg. The highest recorded bottom salinity value is associated with the Major Baltic Inflow of 2014, while other significant salinity peaks align with the Major Baltic Inflows of 1993 and 2002. Low salinity episodes in the Arkona Basin mark the occasions of barotropic outflows of brackish water from the Baltic Sea. In the Bornholm Basin, the bottom salinity record indicates three Major Baltic Inflow events: the first in 1993, followed by 2002, and the most recent in 2014. Following the last Major Baltic Inflow, the bottom salinity in the Bornholm Basin rose to 20 g/kg. Over the subsequent nine years, it has declined to 16 g/kg. The winter of 2023/24 did not experience a Major Baltic Inflow.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00209\n\n**References:**\n\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Mohrholz V, 2018, Major Baltic Inflow Statistics \u2013 Revised. Frontiers in Marine Science, 5:384, doi: 10.3389/fmars.2018.00384\n* Neumann, T., Radtke, H., Seifert, T., 2017. On the importance of Major Baltic In\ufb02ows for oxygenation of the central Baltic Sea, J. Geophys. Res. Oceans, 122, 1090\u20131101, doi:10.1002/2016JC012525.\n* Raudsepp, U., Legeais, J.-F., She, J., Maljutenko, I., Jandt, S., 2018. Baltic inflows. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, doi: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00209", "instrument": null, "keywords": "baltic-omi-wmhe-mbi-bottom-salinity-arkona-bornholm,baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-salinity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Major Baltic Inflow: bottom salinity from Reanalysis"}, "BALTIC_OMI_WMHE_mbi_sto2tz_gotland": {"abstract": "\"_DEFINITION_'\n\nMajor Baltic Inflows bring large volumes of saline and oxygen-rich water into the bottom layers of the deep basins of the central Baltic Sea, i.e. the Gotland Basin. These Major Baltic Inflows occur seldom, sometimes many years apart (Mohrholz, 2018). The Major Baltic Inflow OMI consists of the time series of the bottom layer salinity in the Arkona Basin and in the Bornholm Basin (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm) and the time-depth plot of temperature, salinity and dissolved oxygen concentration in the Gotland Basin. Temperature, salinity and dissolved oxygen profiles in the Gotland Basin enable us to estimate the amount of the Major Baltic Inflow water that has reached central Baltic, the depth interval of which has been the most affected, and how much the oxygen conditions have been improved. \n\n**CONTEXT**\n\nThe Baltic Sea is a huge brackish water basin in Northern Europe whose salinity is controlled by its freshwater budget and by the water exchange with the North Sea (e.g. Neumann et al., 2017). This implies that fresher water lies on top of water with higher salinity. The saline water inflows to the Baltic Sea through the Danish Straits, especially the Major Baltic Inflows, shape hydrophysical conditions in the Gotland Basin of the central Baltic Sea, which in turn have a substantial influence on marine ecology on different trophic levels (Bergen et al., 2018; Raudsepp et al.,2019). In the absence of the Major Baltic Inflows, oxygen in the deeper layers of the Gotland Basin is depleted and replaced by hydrogen sulphide (e.g., Savchuk, 2018). As the Baltic Sea is connected to the North Sea only through very narrow and shallow channels in the Danish Straits, inflows of high salinity and oxygenated water into the Baltic occur only intermittently (e.g., Mohrholz, 2018). Long-lasting periods of oxygen depletion in the deep layers of the central Baltic Sea accompanied by a salinity decline and overall weakening of the vertical stratification are referred to as stagnation periods. Extensive stagnation periods occurred in the 1920s/1930s, in the 1950s/1960s and in the 1980s/beginning of 1990s (Lehmann et al., 2022).\n\n**KEY FINDINGS**\n\nThe Major Baltic Inflows of 1993, 2002, and 2014 (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm) present a distinct signal in the Gotland Basin, influencing water salinity, temperature, and dissolved oxygen up to a depth of 100 meters. Following each event, deep layer salinity in the Gotland Basin increases, reaching peak bottom salinities approximately 1.5 years later, with elevated salinity levels persisting for about three years. Post-2017, salinity below 150 meters has declined, while the halocline has risen, suggesting saline water movement to the Gotland Basin's intermediate layers. Typically, temperatures fall immediately after a Major Baltic Inflow, indicating the descent of cold water from nearby upstream regions to the Gotland Deep's bottom. From 1993 to 1997, deep water temperatures remained relatively low (below 6 \u00b0C). Since 1998, these waters have warmed, with even moderate inflows in 1997/98, 2006/07, and 2018/19 introducing warmer water to the Gotland Basin's bottom layer. From 2019 onwards, water warmer than 7 \u00b0C has filled the layer beneath 100 meters depth. The water temperature below the halocline has risen by approximately 2 \u00b0C since 1993, and the cold intermediate layer's temperature has also increased from 1993 to 2023. Oxygen levels begin to drop sharply after the temporary reoxygenation of the bottom waters. The decline in 2014 was attributed to a shortage of smaller inflows that could bring oxygen-rich water to the Gotland Basin (Neumann et al., 2017) and an increase in biological oxygen demand (Savchuk, 2018; Meier et al., 2018). Additionally, warmer water has accelerated oxygen consumption in the deep layer, leading to increased anoxia. By 2023, oxygen was completely depleted below the depth of 75 metres.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00210\n\n**References:**\n\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Bergen, B., Naumann, M., Herlemann, D.P.R., Gr\u00e4we, U., Labrenz, M., J\u00fcrgens, K., 2018. Impact of a Major inflow event on the composition and distribution of bacterioplankton communities in the Baltic Sea. Frontiers in Marine Science, 5:383, doi: 10.3389/fmars.2018.00383\n* Meier, H.E.M., V\u00e4li, G., Naumann, M., Eilola, K., Frauen, C., 2018. Recently Accelerated Oxygen Consumption Rates Amplify Deoxygenation in the Baltic Sea. , J. Geophys. Res. Oceans, doi:10.1029/2017JC013686|\n* Mohrholz, V., 2018. Major Baltic Inflow Statistics \u2013 Revised. Frontiers in Marine Science, 5:384, DOI: 10.3389/fmars.2018.00384\n* Neumann, T., Radtke, H., Seifert, T., 2017. On the importance of Major Baltic In\ufb02ows for oxygenation of the central Baltic Sea, J. Geophys. Res. Oceans, 122, 1090\u20131101, doi:10.1002/2016JC012525.\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3\n* Savchuk, P. 2018. Large-Scale Nutrient Dynamics in the Baltic Sea, 1970\u20132016. Frontiers in Marine Science, 5:95, doi: 10.3389/fmars.2018.00095\n", "doi": "10.48670/moi-00210", "instrument": null, "keywords": "baltic-omi-wmhe-mbi-sto2tz-gotland,baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,volume-fraction-of-oxygen-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Major Baltic Inflow: time/depth evolution S,T,O2 from Observations Reprocessing"}, "BLKSEA_ANALYSISFORECAST_BGC_007_010": {"abstract": "BLKSEA_ANALYSISFORECAST_BGC_007_010 is the nominal product of the Black Sea Biogeochemistry NRT system and is generated by the NEMO 4.2-BAMHBI modelling system. Biogeochemical Model for Hypoxic and Benthic Influenced areas (BAMHBI) is an innovative biogeochemical model with a 28-variable pelagic component (including the carbonate system) and a 6-variable benthic component ; it explicitely represents processes in the anoxic layer.\nThe product provides analysis and forecast for 3D concentration of chlorophyll, nutrients (nitrate and phosphate), dissolved oxygen, zooplankton and phytoplankton carbon biomass, oxygen-demand-units, net primary production, pH, dissolved inorganic carbon, total alkalinity, and for 2D fields of bottom oxygen concentration (for the North-Western shelf), surface partial pressure of CO2 and surface flux of CO2. These variables are computed on a grid with ~3km x 59-levels resolution, and are provided as daily and monthly means.\n\n**DOI (product):** \nhttps://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010\n\n**References:**\n\n* Gr\u00e9goire, M., Vandenbulcke, L. and Capet, A. (2020) \u201cBlack Sea Biogeochemical Analysis and Forecast (CMEMS Near-Real Time BLACKSEA Biogeochemistry).\u201d Copernicus Monitoring Environment Marine Service (CMEMS). doi: 10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010\"\n", "doi": "10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010", "instrument": null, "keywords": "black-sea,blksea-analysisforecast-bgc-007-010,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "BLKSEA_ANALYSISFORECAST_PHY_007_001": {"abstract": "The BLKSEA_ANALYSISFORECAST_PHY_007_001 is produced with a hydrodynamic model implemented over the whole Black Sea basin, including the Azov Sea, the Bosporus Strait and a portion of the Marmara Sea for the optimal interface with the Mediterranean Sea through lateral open boundary conditions. The model horizontal grid resolution is 1/40\u00b0 in zonal and 1/40\u00b0 in meridional direction (ca. 3 km) and has 121 unevenly spaced vertical levels. The product provides analysis and forecast for 3D potential temperature, salinity, horizontal and vertical currents. Together with the 2D variables sea surface height, bottom potential temperature and mixed layer thickness.\n\n**DOI (Product)**: \nhttps://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS6\n\n**References:**\n\n* Jansen, E., Martins, D., Stefanizzi, L., Ciliberti, S. A., Gunduz, M., Ilicak, M., Lecci, R., Cret\u00ed, S., Causio, S., Aydo\u011fdu, A., Lima, L., Palermo, F., Peneva, E. L., Coppini, G., Masina, S., Pinardi, N., Palazov, A., and Valchev, N. (2022). Black Sea Physical Analysis and Forecast (Copernicus Marine Service BS-Currents, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS5\n", "doi": "10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS6", "instrument": null, "keywords": "black-sea,blksea-analysisforecast-phy-007-001,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Physics Analysis and Forecast"}, "BLKSEA_ANALYSISFORECAST_WAV_007_003": {"abstract": "The wave analysis and forecasts for the Black Sea are produced with the third generation spectral wave model WAM Cycle 6. The hindcast and ten days forecast are produced twice a day on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 81,531. The model takes into account depth refraction, wave breaking, and assimilation of satellite wave and wind data. The system provides a hindcast and ten days forecast with one-hourly output twice a day. The atmospheric forcing is taken from ECMWF analyses and forecast data. Additionally, WAM is forced by surface currents and sea surface height from BLKSEA_ANALYSISFORECAST_PHY_007_001. Monthly statistics are provided operationally on the Product Quality Dashboard following the CMEMS metrics definitions.\n\n**Citation**: \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_WAV_007_003_EAS5\n\n**DOI (Product)**: \nhttps://doi.org/10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5\n\n**References:**\n\n* Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_WAV_007_003_EAS5\n* Ricker, M., Behrens, A., & Staneva, J. (2024). The operational CMEMS wind wave forecasting system of the Black Sea. Journal of Operational Oceanography, 1\u201322. https://doi.org/10.1080/1755876X.2024.2364974\n", "doi": "10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5", "instrument": null, "keywords": "black-sea,blksea-analysisforecast-wav-007-003,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": "proprietary", "missionStartDate": "2021-04-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Waves Analysis and Forecast"}, "BLKSEA_MULTIYEAR_BGC_007_005": {"abstract": "The biogeochemical reanalysis for the Black Sea is produced by the MAST/ULiege Production Unit by means of the BAMHBI biogeochemical model. The workflow runs on the CECI hpc infrastructure (Wallonia, Belgium).\n\n**DOI (product)**:\nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI\n\n**References:**\n\n* Gr\u00e9goire, M., Vandenbulcke, L., & Capet, A. (2020). Black Sea Biogeochemical Reanalysis (CMEMS BS-Biogeochemistry) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_REANALYSIS_BIO_007_005_BAMHBI\n", "doi": "10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI", "instrument": null, "keywords": "black-sea,blksea-multiyear-bgc-007-005,cell-thickness,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Biogeochemistry Reanalysis"}, "BLKSEA_MULTIYEAR_PHY_007_004": {"abstract": "The BLKSEA_MULTIYEAR_PHY_007_004 product provides ocean fields for the Black Sea basin starting from 01/01/1993. The hydrodynamic core is based on the NEMOv4.0 general circulation ocean model, implemented in the BS domain with horizontal resolution of 1/40\u00ba and 121 vertical levels. NEMO is forced by atmospheric fluxes computed from a bulk formulation applied to ECMWF ERA5 atmospheric fields at the resolution of 1/4\u00ba in space and 1-h in time. A heat flux correction through sea surface temperature (SST) relaxation is employed using the ESA-CCI SST-L4 product. This version has an open lateral boundary, a new model characteristic that allows a better inflow/outflow representation across the Bosphorus Strait. The model is online coupled to OceanVar assimilation scheme to assimilate sea level anomaly (SLA) along-track observations from Copernicus and available in situ vertical profiles of temperature and salinity from both SeaDataNet and Copernicus datasets. Upgrades on data assimilation include an improved background error covariance matrix and an observation-based mean dynamic topography for the SLA assimilation.\n\n**DOI (Product)**: \nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004\n\n**References:**\n\n* Lima, L., Aydogdu, A., Escudier, R., Masina, S., Ciliberti, S. A., Azevedo, D., Peneva, E. L., Causio, S., Cipollone, A., Clementi, E., Cret\u00ed, S., Stefanizzi, L., Lecci, R., Palermo, F., Coppini, G., Pinardi, N., & Palazov, A. (2020). Black Sea Physical Reanalysis (CMEMS BS-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004\n", "doi": "10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004", "instrument": null, "keywords": "black-sea,blksea-multiyear-phy-007-004,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Physics Reanalysis"}, "BLKSEA_MULTIYEAR_WAV_007_006": {"abstract": "The wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74,518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data. In addition, the product comprises a monthly climatology dataset based on significant wave height and Tm02 wave period as well as an air-sea-flux dataset.\n\n**Citation**: \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). \n\n**DOI (Product)**: \nhttps://doi.org/10.25423/cmcc/blksea_multiyear_wav_007_006_eas4\n\n**References:**\n\n* Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_WAV_007_006_EAS4\n", "doi": "10.25423/cmcc/blksea_multiyear_wav_007_006_eas4", "instrument": null, "keywords": "black-sea,blksea-multiyear-wav-007-006,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eastward-friction-velocity-at-sea-water-surface,eastward-wave-mixing-momentum-flux-into-sea-water,level-4,marine-resources,marine-safety,multi-year,northward-friction-velocity-at-sea-water-surface,northward-wave-mixing-momentum-flux-into-sea-water,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-roughness-length,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting,wind-from-direction,wind-speed", "license": "proprietary", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Waves Reanalysis"}, "BLKSEA_OMI_HEALTH_oxygen_trend": {"abstract": "**DEFINITION**\n\nThe oxygenation status of the Black Sea open basin is described by three complementary indicators, derived from vertical profiles and spatially averaged over the Black Sea open basin (depth > 50m). (1) The oxygen penetration depth is the depth at which [O2] < 20\u00b5M, expressed in [m]. (2) The oxygen penetration density is the potential density anomaly at the oxygen penetration depth [kg/m\u00b3]. (3) The oxygen inventory is the vertically integrated oxygen content [mol O2/m\u00b2]. The 20\u00b5M threshold was chosen to minimize the indicator sensitivity to sensor\u2019s precision. Those three metrics are complementary: Oxygen penetration depth is more easily understood, but present more spatial variability. Oxygen penetration density helps in dissociating biogeochemical processes from shifts in the physical structure. Although less intuitive, the oxygen inventory is a more integrative diagnostic and its definition is more easily transposed to other areas.\n\n**CONTEXT**\n\nThe Black Sea is permanently stratified, due to the contrast in density between large riverine and Mediterranean inflows. This stratification restrains the ventilation of intermediate and deep waters and confines, within a restricted surface layer, the waters that are oxygenated by photosynthesis and exchanges with the atmosphere. The vertical extent of the oxic layer determines the volume of habitat available for pelagic populations (Ostrovskii and Zatsepin 2011, Sak\u0131nan and G\u00fcc\u00fc 2017) and present spatial and temporal variations (Murray et al. 1989; Tugrul et al. 1992; Konovalov and Murray 2001). At long and mid-term, these variations can be monitored with three metrics (Capet et al. 2016), derived from the vertical profiles that can obtained from traditional ship casts or autonomous Argo profilers (Stanev et al., 2013). A large source of uncertainty associated with the spatial and temporal average of those metrics stems from the small number of Argo floats, scarcely adequate to sample the known spatial variability of those metrics.\n\n**CMEMS KEY FINDINGS**\n\nDuring the past 60 years, the vertical extent of the Black Sea oxygenated layer has narrowed from 140m to 90m (Capet et al. 2016). The Argo profilers active for 2016 suggested an ongoing deoxygenation trend and indicated an average oxygen penetration depth of 72m at the end of 2016, the lowest value recorded during the past 60 years. The oxygenation of subsurface water is closely related to the intensity of cold water formation, an annual ventilation processes which has been recently limited by warmer-than-usual winter air temperature (Capet et al. 2020). In 2017, 2018 and 2020, cold waters formation resulted in a partial reoxygenation of the intermediate layer. Yet, such ventilation has been lacking in winter 2020-2021, and the updated 2021 indicators reveals the lowest oxygen inventory ever reported in this OMI time series. This results in significant detrimental trends now depicted also over the Argo period (2012-2021).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00213\n\n**References:**\n\n* Capet, A., Vandenbulcke, L., & Gr\u00e9goire, M. (2020). A new intermittent regime of convective ventilation threatens the Black Sea oxygenation status. Biogeosciences , 17(24), 6507\u20136525.\n* Capet A, Stanev E, Beckers JM, Murray J, Gr\u00e9goire M. (2016). Decline of the Black Sea oxygen inventory. Biogeosciences. 13:1287-1297.\n* Capet Arthur, Vandenbulcke Luc, Veselka Marinova, Gr\u00e9goire Marilaure. (2018). Decline of the Black Sea oxygen inventory. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Konovalov S, Murray JW. (2001). Variations in the chemistry of the Black Sea on a time scale of decades (1960\u20131995). J Marine Syst. 31: 217\u2013243.\n* Murray J, Jannasch H, Honjo S, Anderson R, Reeburgh W, Top Z, Friederich G, Codispoti L, Izdar E. (1989). Unexpected changes in the oxic/anoxic interface in the Black Sea. Nature. 338: 411\u2013413.\n* Ostrovskii A and Zatsepin A. (2011). Short-term hydrophysical and biological variability over the northeastern Black Sea continental slope as inferred from multiparametric tethered profiler surveys, Ocean Dynam., 61, 797\u2013806, 2011.\n* \u00d6zsoy E and \u00dcnl\u00fcata \u00dc. (1997). Oceanography of the Black Sea: a review of some recent results. Earth-Science Reviews. 42(4):231-72.\n* Sak\u0131nan S, G\u00fcc\u00fc AC. (2017). Spatial distribution of the Black Sea copepod, Calanus euxinus, estimated using multi-frequency acoustic backscatter. ICES J Mar Sci. 74(3):832-846. doi:10.1093/icesjms/fsw183\n* Stanev E, He Y, Grayek S, Boetius A. (2013). Oxygen dynamics in the Black Sea as seen by Argo profiling floats. Geophys Res Lett. 40(12), 3085-3090.\n* Tugrul S, Basturk O, Saydam C, Yilmaz A. (1992). Changes in the hydrochemistry of the Black Sea inferred from water density profiles. Nature. 359: 137-139.\n* von Schuckmann, K. et al. Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography 11, S1\u2013S142 (2018).\n", "doi": "10.48670/moi-00213", "instrument": null, "keywords": "black-sea,blksea-omi-health-oxygen-trend,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,ocean-mole-content-of-dissolved-molecular-oxygen,oceanographic-geographical-features,sea-water-sigma-theta-defined-by-mole-concentration-of-dissolved-molecular-oxygen-in-sea-water-above-threshold,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1955-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Oxygen Trend from Observations Reprocessing"}, "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS BLKSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_WAV_007_006) and the Analysis product (BLKSEA_ANALYSISFORECAST_WAV_007_003).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1979-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, including biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2015, IPCC, 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions (IPCC, 2021).\nSignificant Wave Height mean 99th percentile in the Black Sea region shows west-eastern dependence demonstrating that the highest values of the average annual 99th percentiles are in the areas where high winds and long fetch are simultaneously present. The largest values of the mean 99th percentile in the Black Sea in the southewestern Black Sea are around 3.5 m, while in the eastern part of the basin are around 2.5 m (Staneva et al., 2019a and 2019b).\n\n**CMEMS KEY FINDINGS**\n\nSignificant Wave Height mean 99th percentile in the Black Sea region shows west-eastern dependence with largest values in the southwestern Black Sea, with values as high as 3.5 m, while the 99th percentile values in the eastern part of the basin are around 2.5 m. The Black Sea, the 99th mean percentile for 2002-2019 shows a similar pattern demonstrating that the highest values of the mean annual 99th percentile are in the western Black Sea. This pattern is consistent with the previous studies, e.g. of (Akp\u0131nar and K\u00f6m\u00fcrc\u00fc, 2012; and Akpinar et al., 2016).\nThe anomaly of the 99th percentile in 2020 is mostly negative with values down to ~-45 cm. The highest negative anomalies for 2020 are observed in the southeastern area where the multi-year mean 99th percentile is the lowest. The highest positive anomalies of the 99th percentile in 2020 are located in the southwestern Black Sea and along the eastern coast. The map of anomalies for 2020, presenting alternate bands of positive and negative values depending on latitude, is consistent with the yearly west-east displacement of the tracks of the largest storms. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00214\n\n**References:**\n\n* Akp\u0131nar, A.; K\u00f6m\u00fcrc\u00fc, M.\u02d9I. Wave energy potential along the south-east coasts of the Black Sea. Energy 2012, 42, 289\u2013302.\n* Akp\u0131nar, A., Bing\u00f6lbali, B., Van Vledder, G., 2016. Wind and wave characteristics in the Black Sea based on the SWAN wave model forced with the CFSR winds. Ocean Eng. 126, 276\u2014298, http://dx. doi.org/10.1016/j.oceaneng.2016.09.026.\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2015. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2015 In book: Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Staneva, J. Behrens, A., Gayer G, Ricker M. (2019a) Black sea CMEMS MYP QUID Report\n* Staneva J, Behrens A., Gayer G, Aouf A., (2019b). Synergy between CMEMS products and newly available data from SENTINEL, Section 3.3, In: Schuckmann K,et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, doi: 10.1080/1755876X.2019.1633075.\n", "doi": "10.48670/moi-00214", "instrument": null, "keywords": "black-sea,blksea-omi-seastate-extreme-var-swh-mean-and-anomaly,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Significant Wave Height extreme from Reanalysis"}, "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS BLKSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_PHY_007_004) and the Analysis product (BLKSEA_ANALYSIS_FORECAST_PHYS_007_001).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. Particularly in the Black Sea, ocean-atmospheric processes together with its general cyclonic circulation (Rim Current) play an important role on the sea surface temperature variability (Capet et al. 2012). As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. The 99th mean percentile of sea surface temperature provides a worth information about the variability of the sea surface temperature and warming trends but has not been investigated with details in the Black Sea.\nWhile the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013). Recent studies indicated a warming trend of the sea surface temperature in the Black Sea in the latest years (Mulet et al., 2018; Sakali and Ba\u015fusta, 2018). A specific analysis on the interannual variability of the basin-averaged sea surface temperature revealed a higher positive trend in its eastern region (Ginzburg et al., 2004). For the past three decades, Sakali and Ba\u015fusta (2018) presented an increase in sea surface temperature that varied along both east\u2013west and south\u2013north directions in the Black Sea. \n\n**CMEMS KEY FINDINGS**\n\nThe mean annual 99th percentile in the period 1993\u20132019 exhibits values ranging from 25.50 to 26.50 oC in the western and central regions of the Black Sea. The values increase towards the east, exceeding 27.5 oC. This contrasting west-east pattern may be linked to the basin wide cyclonic circulation. There are regions showing lower values, below 25.75 oC, such as a small area west of Crimean Peninsula in the vicinity of the Sevastopol anticyclone, the Northern Ukraine region, in particular close to the Odessa and the Karkinytska Gulf due to the freshwaters from the land and a narrow area along the Turkish coastline in the south. Results for 2020 show negative anomalies in the area of influence of the Bosporus and the Bulgarian offshore region up to the Crimean peninsula, while the North West shelf exhibits a positive anomaly as in the Eastern basin. The highest positive value is occurring in the Eastern Tukish coastline nearest the Batumi gyre area. This may be related to the variously increase of sea surface temperature in such a way the southern regions have experienced a higher warming.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00216\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Capet, A., Barth, A., Beckers, J. M., & Marilaure, G. (2012). Interannual variability of Black Sea's hydrodynamics and connection to atmospheric patterns. Deep Sea Research Part II: Topical Studies in Oceanography, 77, 128-142. https://doi.org/10.1016/j.dsr2.2012.04.010\n* Ginzburg, A. I.; Kostianoy, A. G.; Sheremet, N. A. (2004). Seasonal and interannual variability of the Black Sea surface temperature as revealed from satellite data (1982\u20132000), Journal of Marine Systems, 52, 33-50. https://doi.org/10.1016/j.jmarsys.2004.05.002.\n* Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Br\u00f6nnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2013. Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 1.1, s5\u2013s13, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sakalli A, Ba\u015fusta N. 2018. Sea surface temperature change in the Black Sea under climate change: A simulation of the sea surface temperature up to 2100. International Journal of Climatology, 38(13), 4687-4698. https://doi.org/10.1002/joc.5688\n", "doi": "10.48670/moi-00216", "instrument": null, "keywords": "black-sea,blksea-omi-tempsal-extreme-var-temp-mean-and-anomaly,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Surface Temperature extreme from Reanalysis"}, "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"abstract": "\"_DEFINITION_'\n\nThe blksea_omi_tempsal_sst_area_averaged_anomalies product for 2023 includes unfiltered Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the Black Sea, and 24-month filtered SST anomalies, obtained by using the X11-seasonal adjustment procedure. This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the evolution of SST anomalies (unfiltered and filtered) over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Anomalies are computed against the 1991-2020 reference period. The 30-year climatology 1991-2020 is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018).\n\n**KEY FINDINGS**\n\nDuring 2023, the Black Sea basin average SST anomaly was ~1.1 \u00b0C above the 1991-2020 climatology, doubling that of previous year (~0.5 \u00b0C). The Black Sea SST monthly anomalies ranged between -1.0/+1.0 \u00b0C. The highest temperature anomaly (~1.8 \u00b0C) was reached in January 2023, while the lowest (~-0.28 \u00b0C) in May. This year, along with 2022, was characterized by milder temperature anomalies with respect to the previous three consecutive years (2018-2020) marked by peaks of ~3 \u00b0C occurred in May 2018, June 2019, and October 2020.\nOver the period 1982-2023, the Black Sea SST has warmed at a rate of 0.065 \u00b1 0.002 \u00b0C/year, which corresponds to an average increase of about 2.7 \u00b0C during these last 42 years.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00217\n\n**References:**\n\n* Buongiorno Nardelli, B., Colella, S. Santoleri, R., Guarracino, M., Kholod, A., 2010. A re-analysis of Black Sea surface temperature. Journal of Marine Systems, 79, Issues 1\u20132, 50-64, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2009.07.001.\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n", "doi": "10.48670/moi-00217", "instrument": null, "keywords": "black-sea,blksea-omi-tempsal-sst-area-averaged-anomalies,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Surface Temperature time series and trend from Observations Reprocessing"}, "BLKSEA_OMI_TEMPSAL_sst_trend": {"abstract": "**DEFINITION**\n\nThe blksea_omi_tempsal_sst_trend product includes the cumulative/net Sea Surface Temperature (SST) trend for the Black Sea over the period 1982-2023, i.e. the rate of change (\u00b0C/year) multiplied by the number years in the timeseries (42). This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the SST trend over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018).\n**KEY FINDINGS**\n\nOver the past four decades (1982-2023), the Black Sea surface temperature (SST) warmed at a rate of 0.065 \u00b1 0.002 \u00b0C per year, corresponding to a mean surface temperature warming of about 2.7 \u00b0C. The spatial pattern of the Black Sea SST trend reveals a general warming tendency, ranging from 0.053 \u00b0C/year to 0.080 \u00b0C/year. The spatial pattern of SST trend is rather homogeneous over the whole basin. Highest values characterize the eastern basin, where the trend reaches the extreme value, while lower values are found close to the western coasts, in correspondence of main rivers inflow. The Black Sea SST trend continues to show the highest intensity among all the other European Seas.\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00218\n\n**References:**\n\n* Buongiorno Nardelli, B., Colella, S. Santoleri, R., Guarracino, M., Kholod, A., 2010. A re-analysis of Black Sea surface temperature. Journal of Marine Systems, 79, Issues 1\u20132, 50-64, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2009.07.001.\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00218", "instrument": null, "keywords": "baltic-sea,blksea-omi-tempsal-sst-trend,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "GLOBAL_ANALYSISFORECAST_BGC_001_028": {"abstract": "The Operational Mercator Ocean biogeochemical global ocean analysis and forecast system at 1/4 degree is providing 10 days of 3D global ocean forecasts updated weekly. The time series is aggregated in time, in order to reach a two full year\u2019s time series sliding window. This product includes daily and monthly mean files of biogeochemical parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production, phytoplankton, zooplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5700 meters.\n\n* NEMO version (v3.6_STABLE)\n* Forcings: GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 at daily frequency. \n* Outputs mean fields are interpolated on a standard regular grid in NetCDF format.\n* Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related [QuID](http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-028.pdf)\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00015", "doi": "10.48670/moi-00015", "instrument": null, "keywords": "cell-height,cell-thickness,cell-width,coastal-marine-environment,forecast,global-analysisforecast-bgc-001-028,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "GLOBAL_ANALYSISFORECAST_PHY_001_024": {"abstract": "The Operational Mercator global ocean analysis and forecast system at 1/12 degree is providing 10 days of 3D global ocean forecasts updated daily. The time series is aggregated in time in order to reach a two full year\u2019s time series sliding window.\n\nThis product includes daily and monthly mean files of temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom over the global ocean. It also includes hourly mean surface fields for sea level height, temperature and currents. The global ocean output files are displayed with a 1/12 degree horizontal resolution with regular longitude/latitude equirectangular projection.\n\n50 vertical levels are ranging from 0 to 5500 meters.\n\nThis product also delivers a special dataset for surface current which also includes wave and tidal drift called SMOC (Surface merged Ocean Current).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00016", "doi": "10.48670/moi-00016", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,forecast,global-analysisforecast-phy-001-024,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Physics Analysis and Forecast"}, "GLOBAL_ANALYSISFORECAST_WAV_001_027": {"abstract": "The operational global ocean analysis and forecast system of M\u00e9t\u00e9o-France with a resolution of 1/12 degree is providing daily analyses and 10 days forecasts for the global ocean sea surface waves. This product includes 3-hourly instantaneous fields of integrated wave parameters from the total spectrum (significant height, period, direction, Stokes drift,...etc), as well as the following partitions: the wind wave, the primary and secondary swell waves.\n \nThe global wave system of M\u00e9t\u00e9o-France is based on the wave model MFWAM which is a third generation wave model. MFWAM uses the computing code ECWAM-IFS-38R2 with a dissipation terms developed by Ardhuin et al. (2010). The model MFWAM was upgraded on november 2014 thanks to improvements obtained from the european research project \u00ab my wave \u00bb (Janssen et al. 2014). The model mean bathymetry is generated by using 2-minute gridded global topography data ETOPO2/NOAA. Native model grid is irregular with decreasing distance in the latitudinal direction close to the poles. At the equator the distance in the latitudinal direction is more or less fixed with grid size 1/10\u00b0. The operational model MFWAM is driven by 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF atmospheric system. The wave spectrum is discretized in 24 directions and 30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM uses the assimilation of altimeters with a time step of 6 hours. The global wave system provides analysis 4 times a day, and a forecast of 10 days at 0:00 UTC. The wave model MFWAM uses the partitioning to split the swell spectrum in primary and secondary swells.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00017\n\n**References:**\n\n* F. Ardhuin, R. Magne, J-F. Filipot, A. Van der Westhyusen, A. Roland, P. Quefeulou, J. M. Lef\u00e8vre, L. Aouf, A. Babanin and F. Collard : Semi empirical dissipation source functions for wind-wave models : Part I, definition and calibration and validation at global scales. Journal of Physical Oceanography, March 2010.\n* P. Janssen, L. Aouf, A. Behrens, G. Korres, L. Cavalieri, K. Christiensen, O. Breivik : Final report of work-package I in my wave project. December 2014.\n", "doi": "10.48670/moi-00017", "instrument": null, "keywords": "coastal-marine-environment,forecast,global-analysisforecast-wav-001-027,global-ocean,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-01-01T03:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Waves Analysis and Forecast"}, "GLOBAL_MULTIYEAR_BGC_001_029": {"abstract": "The biogeochemical hindcast for global ocean is produced at Mercator-Ocean (Toulouse. France). It provides 3D biogeochemical fields since year 1993 at 1/4 degree and on 75 vertical levels. It uses PISCES biogeochemical model (available on the NEMO modelling platform). No data assimilation in this product.\n\n* Latest NEMO version (v3.6_STABLE)\n* Forcings: FREEGLORYS2V4 ocean physics produced at Mercator-Ocean and ERA-Interim atmosphere produced at ECMWF at a daily frequency \n* Outputs: Daily (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production) and monthly (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production. iron. phytoplankton in carbon) 3D mean fields interpolated on a standard regular grid in NetCDF format. The simulation is performed once and for all.\n* Initial conditions: World Ocean Atlas 2013 for nitrate. phosphate. silicate and dissolved oxygen. GLODAPv2 for DIC and Alkalinity. and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00019", "doi": "10.48670/moi-00019", "instrument": null, "keywords": "coastal-marine-environment,global-multiyear-bgc-001-029,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Biogeochemistry Hindcast"}, "GLOBAL_MULTIYEAR_BGC_001_033": {"abstract": "The Low and Mid-Trophic Levels (LMTL) reanalysis for global ocean is produced at [CLS](https://www.cls.fr) on behalf of Global Ocean Marine Forecasting Center. It provides 2D fields of biomass content of zooplankton and six functional groups of micronekton. It uses the LMTL component of SEAPODYM dynamical population model (http://www.seapodym.eu). No data assimilation has been done. This product also contains forcing data: net primary production, euphotic depth, depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers.\n\n**Forcings sources:**\n* Ocean currents and temperature (CMEMS multiyear product)\n* Net Primary Production computed from chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high latitudes (CMEMS biogeochemistry multiyear product)\n\n**Vertical coverage:**\n* Epipelagic layer \n* Upper mesopelagic layer\n* Lower mesopelagic layer (max. 1000m)\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00020\n\n**References:**\n\n* Lehodey P., Murtugudde R., Senina I. (2010). Bridging the gap from ocean models to population dynamics of large marine predators: a model of mid-trophic functional groups. Progress in Oceanography, 84, p. 69-84.\n* Lehodey, P., Conchon, A., Senina, I., Domokos, R., Calmettes, B., Jouanno, J., Hernandez, O., Kloser, R. (2015) Optimization of a micronekton model with acoustic data. ICES Journal of Marine Science, 72(5), p. 1399-1412.\n* Conchon A. (2016). Mod\u00e9lisation du zooplancton et du micronecton marins. Th\u00e8se de Doctorat, Universit\u00e9 de La Rochelle, 136 p.\n", "doi": "10.48670/moi-00020", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity-vertical-mean-over-pelagic-layer,euphotic-zone-depth,global-multiyear-bgc-001-033,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water,northward-sea-water-velocity-vertical-mean-over-pelagic-layer,numerical-model,oceanographic-geographical-features,sea-water-pelagic-layer-bottom-depth,sea-water-potential-temperature-vertical-mean-over-pelagic-layer,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1998-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "GLOBAL_MULTIYEAR_PHY_001_030": {"abstract": "The GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12\u00b0 horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward).\n\nIt is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.\n\nThis product includes daily and monthly mean files for temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom. The global ocean output files are displayed on a standard regular grid at 1/12\u00b0 (approximatively 8 km) and on 50 standard levels.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00021", "doi": "10.48670/moi-00021", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,global-multiyear-phy-001-030,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Physics Reanalysis"}, "GLOBAL_MULTIYEAR_PHY_ENS_001_031": {"abstract": "You can find here the CMEMS Global Ocean Ensemble Reanalysis product at \u00bc degree resolution: monthly means of Temperature, Salinity, Currents and Ice variables for 75 vertical levels, starting from 1993 onward.\n \nGlobal ocean reanalyses are homogeneous 3D gridded descriptions of the physical state of the ocean covering several decades, produced with a numerical ocean model constrained with data assimilation of satellite and in situ observations. These reanalyses are built to be as close as possible to the observations (i.e. realistic) and in agreement with the model physics The multi-model ensemble approach allows uncertainties or error bars in the ocean state to be estimated.\n\nThe ensemble mean may even provide for certain regions and/or periods a more reliable estimate than any individual reanalysis product.\n\nThe four reanalyses, used to create the ensemble, covering \u201caltimetric era\u201d period (starting from 1st of January 1993) during which altimeter altimetry data observations are available:\n * GLORYS2V4 from Mercator Ocean (Fr);\n * ORAS5 from ECMWF;\n * GloSea5 from Met Office (UK);\n * and C-GLORSv7 from CMCC (It);\n \nThese four products provided four different time series of global ocean simulations 3D monthly estimates. All numerical products available for users are monthly or daily mean averages describing the ocean.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00024", "doi": "10.48670/moi-00024", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-multiyear-phy-ens-001-031,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Ensemble Physics Reanalysis"}, "GLOBAL_MULTIYEAR_WAV_001_032": {"abstract": "GLOBAL_REANALYSIS_WAV_001_032 for the global wave reanalysis describing past sea states since years 1980. This product also bears the name of WAVERYS within the GLO-HR MFC for correspondence to other global multi-year products like GLORYS. BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5\u00b0 irregular grid. Average wave quantities derived from this wave spectrum such as the SWH (significant wave height) or the average wave period are delivered on a regular 1/5\u00b0 grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from a geometric sequence of first member 0.035 Hz and a reason 7.5. WAVERYS takes into account oceanic currents from the GLORYS12 physical ocean reanalysis and assimilates SWH observed from historical altimetry missions and directional wave spectra from Sentinel 1 SAR from 2017 onwards. \n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00022", "doi": "10.48670/moi-00022", "instrument": null, "keywords": "coastal-marine-environment,global-multiyear-wav-001-032,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Waves Reanalysis"}, "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly": {"abstract": "\"_DEFINITION_'\n\nNINO34 sub surface temperature anomaly (\u00b0C) is defined as the difference between the subsurface temperature averaged over the 170\u00b0W-120\u00b0W 5\u00b0S,-5\u00b0N area and the climatological reference value over same area (GLOBAL_MULTIYEAR_PHY_ENS_001_031). Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014. \n\n**CONTEXT**\n\nEl Nino Southern Oscillation (ENSO) is one of the most important sources of climatic variability resulting from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations. Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Ni\u00f1o is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Ni\u00f1a (review in Wang et al., 2017). Nino 3.4 sub-surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific el Nino conditions and enable to monitor the evolution the ENSO phase.\n\n**CMEMS KEY FINDINGS **\n\nOver the 1993-2023 period, there were several episodes of strong positive ENSO (el nino) phases in particular during the 1997/1998 winter and the 2015/2016 winter, where NINO3.4 indicator reached positive values larger than 2\u00b0C (and remained above 0.5\u00b0C during more than 6 months). Several La Nina events were also observed like during the 1998/1999 winter and during the 2010/2011 winter. \nThe NINO34 subsurface indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of atmospheric conditions. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00220\n\n**References:**\n\n* Copernicus Marine Service Ocean State Report. (2018). Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00220", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-climvar-enso-tzt-anomaly,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nino 3.4 Temporal Evolution of Vertical Profile of Temperature from Reanalysis"}, "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nNINO34 sea surface temperature anomaly (\u00b0C) is defined as the difference between the sea surface temperature averaged over the 170\u00b0W-120\u00b0W 5\u00b0S,-5\u00b0N area and the climatological reference value over same area (GLOBAL_MULTIYEAR_PHY_ENS_001_031) . Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014. El Nino or La Nina events are defined when the NINO3.4 SST anomalies exceed +/- 0.5\u00b0C during a period of six month.\n\n**CONTEXT**\n\nEl Nino Southern Oscillation (ENSO) is one of the most important source of climatic variability resulting from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations. Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Ni\u00f1o is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Ni\u00f1a (review in Wang et al., 2017). Nino 3.4 Sea surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific El Nino conditions and enable to monitor the evolution the ENSO phase.\n\n**CMEMS KEY FINDINGS**\n\nOver the 1993-2023 period, there were several episodes of strong positive ENSO phases in particular in 1998 and 2016, where NINO3.4 indicator reached positive values larger than 2\u00b0C (and remained above 0.5\u00b0C during more than 6 months). Several La Nina events were also observed like in 2000 and 2008. \nThe NINO34 indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of meteorological conditions. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00219\n\n**References:**\n\n* Copernicus Marine Service Ocean State Report. (2018). Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00219", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-climvar-enso-sst-area-averaged-anomalies,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nino 3.4 Sea Surface Temperature time series from Reanalysis"}, "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated": {"abstract": "**DEFINITION**\n\nThe global yearly ocean CO2 sink represents the ocean uptake of CO2 from the atmosphere computed over the whole ocean. It is expressed in PgC per year. The ocean monitoring index is presented for the period 1985 to year-1. The yearly estimate of the ocean CO2 sink corresponds to the mean of a 100-member ensemble of CO2 flux estimates (Chau et al. 2022). The range of an estimate with the associated uncertainty is then defined by the empirical 68% interval computed from the ensemble.\n\n**CONTEXT**\n\nSince the onset of the industrial era in 1750, the atmospheric CO2 concentration has increased from about 277\u00b13 ppm (Joos and Spahni, 2008) to 412.44\u00b10.1 ppm in 2020 (Dlugokencky and Tans, 2020). By 2011, the ocean had absorbed approximately 28 \u00b1 5% of all anthropogenic CO2 emissions, thus providing negative feedback to global warming and climate change (Ciais et al., 2013). The ocean CO2 sink is evaluated every year as part of the Global Carbon Budget (Friedlingstein et al. 2022). The uptake of CO2 occurs primarily in response to increasing atmospheric levels. The global flux is characterized by a significant variability on interannual to decadal time scales largely in response to natural climate variability (e.g., ENSO) (Friedlingstein et al. 2022, Chau et al. 2022). \n\n**CMEMS KEY FINDINGS**\n\nThe rate of change of the integrated yearly surface downward flux has increased by 0.04\u00b10.03e-1 PgC/yr2 over the period 1985 to year-1. The yearly flux time series shows a plateau in the 90s followed by an increase since 2000 with a growth rate of 0.06\u00b10.04e-1 PgC/yr2. In 2021 (resp. 2020), the global ocean CO2 sink was 2.41\u00b10.13 (resp. 2.50\u00b10.12) PgC/yr. The average over the full period is 1.61\u00b10.10 PgC/yr with an interannual variability (temporal standard deviation) of 0.46 PgC/yr. In order to compare these fluxes to Friedlingstein et al. (2022), the estimate of preindustrial outgassing of riverine carbon of 0.61 PgC/yr, which is in between the estimate by Jacobson et al. (2007) (0.45\u00b10.18 PgC/yr) and the one by Resplandy et al. (2018) (0.78\u00b10.41 PgC/yr) needs to be added. A full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020) and in Chau et al. (2022).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00223\n\n**References:**\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Ciais, P., Sabine, C., Govindasamy, B., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Que\u0301re\u0301, C., Myneni, R., Piao, S., and Thorn- ton, P.: Chapter 6: Carbon and Other Biogeochemical Cycles, in: Climate Change 2013 The Physical Science Basis, edited by: Stocker, T., Qin, D., and Platner, G.-K., Cambridge University Press, Cambridge, 2013.\n* Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, National Oceanic and Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL), http://www.esrl. noaa.gov/gmd/ccgg/trends/global.html, last access: 11 March 2022.\n* Joos, F. and Spahni, R.: Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years, P. Natl. Acad. Sci. USA, 105, 1425\u20131430, https://doi.org/10.1073/pnas.0707386105, 2008.\n* Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Qu\u00e9r\u00e9, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., Bopp, L., Chau, T. T. T., Chevallier, F., Chini, L. P., Cronin, M., Currie, K. I., Decharme, B., Djeutchouang, L. M., Dou, X., Evans, W., Feely, R. A., Feng, L., Gasser, T., Gilfillan, D., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., G\u00fcrses, \u00d6., Harris, I., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Luijkx, I. T., Jain, A., Jones, S. D., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., K\u00f6rtzinger, A., Landsch\u00fctzer, P., Lauvset, S. K., Lef\u00e8vre, N., Lienert, S., Liu, J., Marland, G., McGuire, P. C., Melton, J. R., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y., Ono, T., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., R\u00f6denbeck, C., Rosan, T. M., Schwinger, J., Schwingshackl, C., S\u00e9f\u00e9rian, R., Sutton, A. J., Sweeney, C., Tanhua, T., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F., van der Werf, G. R., Vuichard, N., Wada, C., Wanninkhof, R., Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, C., Yue, X., Zaehle, S., and Zeng, J.: Global Carbon Budget 2021, Earth Syst. Sci. Data, 14, 1917\u20132005, https://doi.org/10.5194/essd-14-1917-2022, 2022.\n* Jacobson, A. R., Mikaloff Fletcher, S. E., Gruber, N., Sarmiento, J. L., and Gloor, M. (2007), A joint atmosphere-ocean inversion for surface fluxes of carbon dioxide: 1. Methods and global-scale fluxes, Global Biogeochem. Cycles, 21, GB1019, doi:10.1029/2005GB002556.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* Resplandy, L., Keeling, R. F., R\u00f6denbeck, C., Stephens, B. B., Khatiwala, S., Rodgers, K. B., Long, M. C., Bopp, L. and Tans, P. P.: Revision of global carbon fluxes based on a reassessment of oceanic and riverine carbon transport. Nature Geoscience, 11(7), p.504, 2018.\n", "doi": "10.48670/moi-00223", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-co2-flux-integrated,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Yearly CO2 Sink from Multi-Observations Reprocessing"}, "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged": {"abstract": "**DEFINITION**\n\nOcean acidification is quantified by decreases in pH, which is a measure of acidity: a decrease in pH value means an increase in acidity, that is, acidification. The observed decrease in ocean pH resulting from increasing concentrations of CO2 is an important indicator of global change. The estimate of global mean pH builds on a reconstruction methodology, \n* Obtain values for alkalinity based on the so called \u201clocally interpolated alkalinity regression (LIAR)\u201d method after Carter et al., 2016; 2018. \n* Build on surface ocean partial pressure of carbon dioxide (CMEMS product: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) obtained from an ensemble of Feed-Forward Neural Networks (Chau et al. 2022) which exploit sampling data gathered in the Surface Ocean CO2 Atlas (SOCAT) (https://www.socat.info/)\n* Derive a gridded field of ocean surface pH based on the van Heuven et al., (2011) CO2 system calculations using reconstructed pCO2 (MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) and alkalinity.\nThe global mean average of pH at yearly time steps is then calculated from the gridded ocean surface pH field. It is expressed in pH unit on total hydrogen ion scale. In the figure, the amplitude of the uncertainty (1\u03c3 ) of yearly mean surface sea water pH varies at a range of (0.0023, 0.0029) pH unit (see Quality Information Document for more details). The trend and uncertainty estimates amount to -0.0017\u00b10.0004e-1 pH units per year.\nThe indicator is derived from in situ observations of CO2 fugacity (SOCAT data base, www.socat.info, Bakker et al., 2016). These observations are still sparse in space and time. Monitoring pH at higher space and time resolutions, as well as in coastal regions will require a denser network of observations and preferably direct pH measurements. \nA full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020).\n\n**CONTEXT**\n\nThe decrease in surface ocean pH is a direct consequence of the uptake by the ocean of carbon dioxide. It is referred to as ocean acidification. The International Panel on Climate Change (IPCC) Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems (2011) defined Ocean Acidification as \u201ca reduction in the pH of the ocean over an extended period, typically decades or longer, which is caused primarily by uptake of carbon dioxide from the atmosphere, but can also be caused by other chemical additions or subtractions from the ocean\u201d. The pH of contemporary surface ocean waters is already 0.1 lower than at pre-industrial times and an additional decrease by 0.33 pH units is projected over the 21st century in response to the high concentration pathway RCP8.5 (Bopp et al., 2013). Ocean acidification will put marine ecosystems at risk (e.g. Orr et al., 2005; Gehlen et al., 2011; Kroeker et al., 2013). The monitoring of surface ocean pH has become a focus of many international scientific initiatives (http://goa-on.org/) and constitutes one target for SDG14 (https://sustainabledevelopment.un.org/sdg14). \n\n**CMEMS KEY FINDINGS**\n\nSince the year 1985, global ocean surface pH is decreasing at a rate of -0.0017\u00b10.0004e-1 per year. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00224\n\n**References:**\n\n* Bakker, D. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383-413, https://doi.org/10.5194/essd-8-383-2016, 2016.\n* Bopp, L. et al.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225\u20136245, doi: 10.5194/bg-10-6225-2013, 2013.\n* Carter, B.R., et al.: Updated methods for global locally interpolated estimation of alkalinity, pH, and nitrate, Limnol. Oceanogr.: Methods 16, 119\u2013131, 2018.\n* Carter, B. R., et al.: Locally interpolated alkalinity regression for global alkalinity estimation. Limnol. Oceanogr.: Methods 14: 268\u2013277. doi:10.1002/lom3.10087, 2016.\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022. Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* IPCC, 2011: Workshop Report of the Intergovernmental Panel on Climate Change Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems. [Field, C.B., V. Barros, T.F. Stocker, D. Qin, K.J. Mach, G.-K. Plattner, M.D. Mastrandrea, M. Tignor and K.L. Ebi (eds.)]. IPCC Working Group II Technical Support Unit, Carnegie Institution, Stanford, California, United States of America, pp.164.\n* Kroeker, K. J. et al.: Meta- analysis reveals negative yet variable effects of ocean acidifica- tion on marine organisms, Ecol. Lett., 13, 1419\u20131434, 2010.\n* Orr, J. C. et al.: Anthropogenic ocean acidification over the twenty-first century and its impact on cal- cifying organisms, Nature, 437, 681\u2013686, 2005.\n* van Heuven, S., et al.: MATLAB program developed for CO2 system calculations, ORNL/CDIAC-105b, Carbon Dioxide Inf. Anal. Cent., Oak Ridge Natl. Lab., US DOE, Oak Ridge, Tenn., 2011.\n", "doi": "10.48670/moi-00224", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-ph-area-averaged,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean acidification - mean sea water pH time series and trend from Multi-Observations Reprocessing"}, "GLOBAL_OMI_HEALTH_carbon_ph_trend": {"abstract": "**DEFINITION**\n\nThis ocean monitoring indicator (OMI) consists of annual mean rates of changes in surface ocean pH (yr-1) computed at 0.25\u00b0\u00d70.25\u00b0 resolution from 1985 until the last year. This indicator is derived from monthly pH time series distributed with the Copernicus Marine product MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008 (Chau et al., 2022a). For each grid cell, a linear least-squares regression was used to fit a linear function of pH versus time, where the slope (\u03bc) and residual standard deviation (\u03c3) are defined as estimates of the long-term trend and associated uncertainty. Finally, the estimates of pH associated with the highest uncertainty, i.e., \u03c3-to-\u00b5 ratio over a threshold of 1 0%, are excluded from the global trend map (see QUID document for detailed description and method illustrations). This threshold is chosen at the 90th confidence level of all ratio values computed across the global ocean.\n\n**CONTEXT**\n\nA decrease in surface ocean pH (i.e., ocean acidification) is primarily a consequence of an increase in ocean uptake of atmospheric carbon dioxide (CO2) concentrations that have been augmented by anthropogenic emissions (Bates et al, 2014; Gattuso et al, 2015; P\u00e9rez et al, 2021). As projected in Gattuso et al (2015), \u201cunder our current rate of emissions, most marine organisms evaluated will have very high risk of impacts by 2100 and many by 2050\u201d. Ocean acidification is thus an ongoing source of concern due to its strong influence on marine ecosystems (e.g., Doney et al., 2009; Gehlen et al., 2011; P\u00f6rtner et al. 2019). Tracking changes in yearly mean values of surface ocean pH at the global scale has become an important indicator of both ocean acidification and global change (Gehlen et al., 2020; Chau et al., 2022b). In line with a sustained establishment of ocean measuring stations and thus a rapid increase in observations of ocean pH and other carbonate variables (e.g. dissolved inorganic carbon, total alkalinity, and CO2 fugacity) since the last decades (Bakker et al., 2016; Lauvset et al., 2021), recent studies including Bates et al (2014), Lauvset et al (2015), and P\u00e9rez et al (2021) put attention on analyzing secular trends of pH and their drivers from time-series stations to ocean basins. This OMI consists of the global maps of long-term pH trends and associated 1\u03c3-uncertainty derived from the Copernicus Marine data-based product of monthly surface water pH (Chau et al., 2022a) at 0.25\u00b0\u00d70.25\u00b0 grid cells over the global ocean.\n\n**CMEMS KEY FINDINGS**\n\nSince 1985, pH has been decreasing at a rate between -0.0008 yr-1 and -0.0022 yr-1 over most of the global ocean basins. Tropical and subtropical regions, the eastern equatorial Pacific excepted, show pH trends falling in the interquartile range of all the trend estimates (between -0.0012 yr-1 and -0.0018 yr-1). pH over the eastern equatorial Pacific decreases much faster, reaching a growth rate larger than -0.0024 yr-1. Such a high rate of change in pH is also observed over a sector south of the Indian Ocean. Part of the polar and subpolar North Atlantic and the Southern Ocean has no significant trend. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00277\n\n**References:**\n\n* Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O'Brien, K. M., Olsen, A., Smith, K., Cosca, C., Harasawa, S., Jones, S. D., Nakaoka, S.-I. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383\u2013413, DOI:10.5194/essd-8-383- 2016, 2016.\n* Bates, N. R., Astor, Y. M., Church, M. J., Currie, K., Dore, J. E., Gonzalez-Davila, M., Lorenzoni, L., Muller-Karger, F., Olafsson, J., and Magdalena Santana-Casiano, J.: A Time-Series View of Changing Surface Ocean Chemistry Due to Ocean Uptake of Anthropogenic CO2 and Ocean Acidification, Oceanography, 27, 126\u2013141, 2014.\n* Chau, T. T. T., Gehlen, M., Chevallier, F. : Global Ocean Surface Carbon: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008, E.U. Copernicus Marine Service Information, DOI:10.48670/moi-00047, 2022a.\n* Chau, T. T. T., Gehlen, M., Chevallier, F.: Global mean seawater pH (GLOBAL_OMI_HEALTH_carbon_ph_area_averaged), E.U. Copernicus Marine Service Information, DOI: 10.48670/moi-00224, 2022b.\n* Doney, S. C., Balch, W. M., Fabry, V. J., and Feely, R. A.: Ocean Acidification: A critical emerging problem for the ocean sciences, Oceanography, 22, 16\u201325, 2009.\n* Gattuso, J-P., Alexandre Magnan, Rapha\u00ebl Bill\u00e9, William WL Cheung, Ella L. Howes, Fortunat Joos, Denis Allemand et al. \"\"Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios.\"\" Science 349, no. 6243 (2015).\n* Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Chau T T T., Conchon A., Denvil-Sommer A., Chevallier F., Vrac M., Mejia C. : Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI:10.1080/1755876X.2020.1785097, 2020.\n* Lauvset, S. K., Gruber, N., Landsch\u00fctzer, P., Olsen, A., and Tjiputra, J.: Trends and drivers in global surface ocean pH over the past 3 decades, Biogeosciences, 12, 1285\u20131298, DOI:10.5194/bg-12-1285-2015, 2015.\n* Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., \u00c1lvarez, M., Becker, S., Brown, P. J., Carter, B. R., Cotrim da Cunha, L., Feely, R. A., van Heuven, S., Hoppema, M., Ishii, M., Jeansson, E., Jutterstr\u00f6m, S., Jones, S. D., Karlsen, M. K., Lo Monaco, C., Michaelis, P., Murata, A., P\u00e9rez, F. F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Velo, A., Wanninkhof, R., Woosley, R. J., and Key, R. M.: An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2021, Earth Syst. Sci. Data, 13, 5565\u20135589, DOI:10.5194/essd-13-5565-2021, 2021.\n* P\u00f6rtner, H. O. et al. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (Wiley IPCC Intergovernmental Panel on Climate Change, Geneva, 2019).\n* P\u00e9rez FF, Olafsson J, \u00d3lafsd\u00f3ttir SR, Fontela M, Takahashi T. Contrasting drivers and trends of ocean acidification in the subarctic Atlantic. Sci Rep 11, 13991, DOI:10.1038/s41598-021-93324-3, 2021.\n", "doi": "10.48670/moi-00277", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-ph-trend,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,trend-of-surface-ocean-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global ocean acidification - mean sea water pH trend map from Multi-Observations Reprocessing"}, "GLOBAL_OMI_NATLANTIC_amoc_26N_profile": {"abstract": "**DEFINITION**\n\nThe Atlantic Meridional Overturning profile at 26.5N is obtained by integrating the meridional transport at 26.5 N across the Atlantic basin (zonally) and then doing a cumulative integral in depth. A climatological mean is then taken over time. This is done by using GLOBAL_MULTIYEAR_PHY_ENS_001_031 over the whole time period (1993-2023) and over the period for which there are comparable observations (Apr 2004-Mar 2023). The observations come from the RAPID array (Smeed et al, 2017). \n\n**CONTEXT**\n\nThe Atlantic Meridional Overturning Circulation (AMOC) transports heat northwards in the Atlantic and plays a key role in regional and global climate (Srokosz et al, 2012). There is a northwards transport in the upper kilometer resulting from northwards flow in the Gulf Stream and wind-driven Ekman transport, and southwards flow in the ocean interior and in deep western boundary currents (Srokosz et al, 2012). There are uncertainties in the deep profile associated with how much transport is returned in the upper (1-3km) or lower (3-5km) North Atlantic deep waters (Roberts et al 2013, Sinha et al 2018).\n\n**CMEMS KEY FINDINGS** \n\nThe AMOC strength at 1000m is found to be 17.0 \u00b1 3.2 Sv (1 Sverdrup=106m3/s; range is 2 x standard deviation of multi-product). See also Jackson et al (2018).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00231\n\n**References:**\n\n* Jackson, L., C. Dubois, S. Masina, A Storto and H Zuo, 2018: Atlantic Meridional Overturning Circulation. In Copernicus Marine Service Ocean State Report, Issue 2. Journal of Operational Oceanography , 11:sup1, S65-S66, 10.1080/1755876X.2018.1489208\n* Roberts, C. D., J. Waters, K. A. Peterson, M. D. Palmer, G. D. McCarthy, E. Frajka\u2010Williams, K. Haines, D. J. Lea, M. J. Martin, D. Storkey, E. W. Blockley and H. Zuo (2013), Atmosphere drives recent interannual variability of the Atlantic meridional overturning circulation at 26.5\u00b0N, Geophys. Res. Lett., 40, 5164\u20135170 doi: 10.1002/grl.50930.\n* Sinha, B., Smeed, D.A., McCarthy, G., Moat, B.I., Josey, S.A., Hirschi, J.J.-M., Frajka-Williams, E., Blaker, A.T., Rayner, D. and Madec, G. (2018), The accuracy of estimates of the overturning circulation from basin-wide mooring arrays. Progress in Oceanography, 160. 101-123\n* Smeed D., McCarthy G., Rayner D., Moat B.I., Johns W.E., Baringer M.O. and Meinen C.S. (2017). Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2017. British Oceanographic Data Centre - Natural Environment Research Council, UK. doi: 10.5285/5acfd143-1104-7b58-e053-6c86abc0d94b\n* Srokosz, M., M. Baringer, H. Bryden, S. Cunningham, T. Delworth, S. Lozier, J. Marotzke, and R. Sutton, 2012: Past, Present, and Future Changes in the Atlantic Meridional Overturning Circulation. Bull. Amer. Meteor. Soc., 93, 1663\u20131676, https://doi.org/10.1175/BAMS-D-11-00151.1\n", "doi": "10.48670/moi-00231", "instrument": null, "keywords": "amoc-cglo,amoc-glor,amoc-glos,amoc-mean,amoc-oras,amoc-std,coastal-marine-environment,global-ocean,global-omi-natlantic-amoc-26n-profile,marine-resources,marine-safety,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Meridional Overturning Circulation AMOC profile at 26N from Reanalysis"}, "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries": {"abstract": "**DEFINITION**\n\nThe Atlantic Meridional Overturning strength at 26.5N is obtained by integrating the meridional transport at 26.5 N across the Atlantic basin (zonally) and then doing a cumulative integral in depth by using GLOBAL_MULTIYEAR_PHY_ENS_001_031 . The maximum value in depth is then taken as the strength in Sverdrups (Sv=1x106m3/s). The observations come from the RAPID array (Smeed et al, 2017).\n\n**CONTEXT**\n\nThe Atlantic Meridional Overturning Circulation (AMOC) transports heat northwards in the Atlantic and plays a key role in regional and global climate (Srokosz et al, 2012). There is a northwards transport in the upper kilometer resulting from northwards flow in the Gulf Stream and wind-driven Ekman transport, and southwards flow in the ocean interior and in deep western boundary currents (Srokosz et al, 2012). The observations have revealed variability at monthly to decadal timescales including a temporary weakening in 2009/10 (McCarthy et al, 2012) and a decrease from 2005-2012 (Smeed et al, 2014; Smeed et al, 2018). Other studies have suggested that this weakening may be a result of variability (Smeed et al, 2014; Jackson et al 2017).\n\n**CMEMS KEY FINDINGS **\n\nThe AMOC strength exhibits significant variability on many timescales with a temporary weakening in 2009/10. There has been a weakening from 2005-2012 (-0.67 Sv/year, (p=0.03) in the observations and -0.53 Sv/year (p=0.04) in the multi-product mean). The multi-product suggests an earlier increase from 2001-2006 (0.48 Sv/yr, p=0.04), and a weakening in 1998-99, however before this period there is significant uncertainty. This indicates that the changes observed are likely to be variability rather than an ongoing trend (see also Jackson et al, 2018).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00232\n\n**References:**\n\n* Jackson, L. C., Peterson, K. A., Roberts, C. D. & Wood, R. A. (2016). Recent slowing of Atlantic overturning circulation as a recovery from earlier strengthening. Nature Geosci, 9, 518\u2014522\n* Jackson, L., C. Dubois, S. Masina, A Storto and H Zuo, 2018: Atlantic Meridional Overturning Circulation. In Copernicus Marine Service Ocean State Report, Issue 2. Journal of Operational Oceanography , 11:sup1, S65-S66, 10.1080/1755876X.2018.1489208\n* McCarthy, G., Frajka-Williams, E., Johns, W. E., Baringer, M. O., Meinen, C. S., Bryden, H. L., Rayner, D., Duchez, A., Roberts, C. & Cunningham, S. A. (2012). Observed interannual variability of the Atlantic meridional overturning circulation at 26.5\u00b0N. Geophys. Res. Lett., 39, L19609+\n* Smeed, D. A., McCarthy, G. D., Cunningham, S. A., Frajka-Williams, E., Rayner, D., Johns, W. E., Meinen, C. S., Baringer, M. O., Moat, B. I., Duchez, A. & Bryden, H. L. (2014). Observed decline of the Atlantic meridional overturning circulation 2004&2012. Ocean Science, 10, 29--38.\n* Smeed D., McCarthy G., Rayner D., Moat B.I., Johns W.E., Baringer M.O. and Meinen C.S. (2017). Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2017. British Oceanographic Data Centre - Natural Environment Research Council, UK. doi: 10.5285/5acfd143-1104-7b58-e053-6c86abc0d94b\n* Smeed, D. A., Josey, S. A., Beaulieu, C., Johns, W. E., Moat, B. I., Frajka-Williams, E., Rayner, D., Meinen, C. S., Baringer, M. O., Bryden, H. L. & McCarthy, G. D. (2018). The North Atlantic Ocean Is in a State of Reduced Overturning. Geophys. Res. Lett., 45, 2017GL076350+. doi: 10.1002/2017gl076350\n* Srokosz, M., M. Baringer, H. Bryden, S. Cunningham, T. Delworth, S. Lozier, J. Marotzke, and R. Sutton, 2012: Past, Present, and Future Changes in the Atlantic Meridional Overturning Circulation. Bull. Amer. Meteor. Soc., 93, 1663\u20131676, https://doi.org/10.1175/BAMS-D-11-00151.1\n", "doi": "10.48670/moi-00232", "instrument": null, "keywords": "amoc-cglo,amoc-glor,amoc-glos,amoc-mean,amoc-oras,amoc-std,coastal-marine-environment,global-ocean,global-omi-natlantic-amoc-max26n-timeseries,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Meridional Overturning Circulation AMOC timeseries at 26N from Reanalysis"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000": {"abstract": "**DEFINITION**\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005, the upper (0-2000m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 1.0 \u00b1 0.1 W/m2. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00235\n\n**References:**\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00235", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-area-averaged-anomalies-0-2000,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_300": {"abstract": "**DEFINITION**\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005, the near-surface (0-300m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 0.4 \u00b1 0.1 W/m2. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00233\n\n**References:**\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00233", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-area-averaged-anomalies-0-300,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content (0-300m) from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_700": {"abstract": "**DEFINITION**\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005, the upper (0-700m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 0.6 \u00b1 0.1 W/m2. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00234\n\n**References:**\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00234", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-area-averaged-anomalies-0-700,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content (0-700m) from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_OHC_trend": {"abstract": "**DEFINITION**\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\n**CMEMS KEY FINDINGS**\n\nRegional trends for the period 2005-2019 from the Copernicus Marine Service multi-ensemble approach show warming at rates ranging from the global mean average up to more than 8 W/m2 in some specific regions (e.g. northern hemisphere western boundary current regimes). There are specific regions where a negative trend is observed above noise at rates up to about -5 W/m2 such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00236\n\n**References:**\n\n* Capotondi, A., Wittenberg, A.T., Kug, J.-S., Takahashi, K. and McPhaden, M.J. (2020). ENSO Diversity. In El Ni\u00f1o Southern Oscillation in a Changing Climate (eds M.J. McPhaden, A. Santoso and W. Cai). https://doi.org/10.1002/9781119548164.ch4\n* Dubois et al., 2018 : Changes in the North Atlantic. Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s1\u2013s142, DOI: 10.1080/1755876X.2018.1489208\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00236", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-trend,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content trend map from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000": {"abstract": "**DEFINITION**\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). \n\n**CONTEXT**\n\nThe global mean sea level is reflecting changes in the Earth\u2019s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005 the upper (0-2000m) near-global (60\u00b0S-60\u00b0N) thermosteric sea level rises at a rate of 1.3\u00b10.2 mm/year. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00240\n\n**References:**\n\n* Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. CifuentesJara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* WCRP Global Sea Level Group, 2018: Global sea-level budget: 1993-present. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* von Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n", "doi": "10.48670/moi-00240", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-sl-thsl-area-averaged-anomalies-0-2000,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,thermosteric-change-in-mean-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Thermosteric Sea Level anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700": {"abstract": "**DEFINITION**\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). \n\n**CONTEXT**\n\nThe global mean sea level is reflecting changes in the Earth\u2019s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005 the upper (0-700m) near-global (60\u00b0S-60\u00b0N) thermosteric sea level rises at a rate of 0.9\u00b10.1 mm/year. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00239\n\n**References:**\n\n* Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. CifuentesJara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* WCRP Global Sea Level Group, 2018: Global sea-level budget: 1993-present. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* von Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n", "doi": "10.48670/moi-00239", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-sl-thsl-area-averaged-anomalies-0-700,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,thermosteric-change-in-mean-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Thermosteric Sea Level anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_SL_thsl_trend": {"abstract": "**DEFINITION**\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m).\n\n**CONTEXT**\n\nMost of the interannual variability and trends in regional sea level is caused by changes in steric sea level. At mid and low latitudes, the steric sea level signal is essentially due to temperature changes, i.e. the thermosteric effect (Stammer et al., 2013, Meyssignac et al., 2016). Salinity changes play only a local role. Regional trends of thermosteric sea level can be significantly larger compared to their globally averaged versions (Storto et al., 2018). Except for shallow shelf sea and high latitudes (> 60\u00b0 latitude), regional thermosteric sea level variations are mostly related to ocean circulation changes, in particular in the tropics where the sea level variations and trends are the most intense over the last two decades. \n\n**CMEMS KEY FINDINGS**\n\nSignificant (i.e. when the signal exceeds the noise) regional trends for the period 2005-2019 from the Copernicus Marine Service multi-ensemble approach show a thermosteric sea level rise at rates ranging from the global mean average up to more than 8 mm/year. There are specific regions where a negative trend is observed above noise at rates up to about -8 mm/year such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00241\n\n**References:**\n\n* Capotondi, A., Wittenberg, A.T., Kug, J.-S., Takahashi, K. and McPhaden, M.J. (2020). ENSO Diversity. In El Ni\u00f1o Southern Oscillation in a Changing Climate (eds M.J. McPhaden, A. Santoso and W. Cai). https://doi.org/10.1002/9781119548164.ch4\n* Dubois et al., 2018 : Changes in the North Atlantic. Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s1\u2013s142, DOI: 10.1080/1755876X.2018.1489208\n* Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Stammer D, Cazenave A, Ponte RM, Tamisiea ME (2013) Causes for contemporary regional sea level changes. Ann Rev Mar Sci 5:21\u201346. doi:10.1146/annurev-marine-121211-172406\n* Meyssignac, B., C. G. Piecuch, C. J. Merchant, M.-F. Racault, H. Palanisamy, C. MacIntosh, S. Sathyendranath, R. Brewin, 2016: Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993\u20132011\n", "doi": "10.48670/moi-00241", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-sl-thsl-trend,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Thermosteric Sea Level trend map from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_Tyz_trend": {"abstract": "**DEFINITION**\n\nThe linear change of zonal mean subsurface temperature over the period 1993-2019 at each grid point (in depth and latitude) is evaluated to obtain a global mean depth-latitude plot of subsurface temperature trend, expressed in \u00b0C.\nThe linear change is computed using the slope of the linear regression at each grid point scaled by the number of time steps (27 years, 1993-2019). A multi-product approach is used, meaning that the linear change is first computed for 5 different zonal mean temperature estimates. The average linear change is then computed, as well as the standard deviation between the five linear change computations. The evaluation method relies in the study of the consistency in between the 5 different estimates, which provides a qualitative estimate of the robustness of the indicator. See Mulet et al. (2018) for more details.\n\n**CONTEXT**\n\nLarge-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions, while the deeper ocean temperature in the main thermocline and below varies due to many dynamical forcing mechanisms (Bindoff et al., 2019). Together with ocean acidification and deoxygenation (IPCC, 2019), ocean warming can lead to dramatic changes in ecosystem assemblages, biodiversity, population extinctions, coral bleaching and infectious disease, change in behavior (including reproduction), as well as redistribution of habitat (e.g. Gattuso et al., 2015, Molinos et al., 2016, Ramirez et al., 2017). Ocean warming also intensifies tropical cyclones (Hoegh-Guldberg et al., 2018; Trenberth et al., 2018; Sun et al., 2017).\n\n**CMEMS KEY FINDINGS**\n\nThe results show an overall ocean warming of the upper global ocean over the period 1993-2019, particularly in the upper 300m depth. In some areas, this warming signal reaches down to about 800m depth such as for example in the Southern Ocean south of 40\u00b0S. In other areas, the signal-to-noise ratio in the deeper ocean layers is less than two, i.e. the different products used for the ensemble mean show weak agreement. However, interannual-to-decadal fluctuations are superposed on the warming signal, and can interfere with the warming trend. For example, in the subpolar North Atlantic decadal variations such as the so called \u2018cold event\u2019 prevail (Dubois et al., 2018; Gourrion et al., 2018), and the cumulative trend over a quarter of a decade does not exceed twice the noise level below about 100m depth.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00244\n\n**References:**\n\n* Dubois, C., K. von Schuckmann, S. Josey, A. Ceschin, 2018: Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208.\n* Gattuso, J-P., et al. (2015): Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, no. 6243.\n* Gourrion, J., J. Deshayes, M. Juza, T. Szekely, J. Tontore, 2018: A persisting regional cold and fresh water anomaly in the Northern Atlantic. In: Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208.\n* Hoegh-Guldberg, O., et al., 2018: Impacts of 1.5\u00baC Global Warming on Natural and Human Systems. In: Global Warming of 1.5\u00b0C. An IPCC Special Report on the impacts of global warming of 1.5\u00b0C above preindustrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. P\u00f6rtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma- Okia, C. P\u00e9an, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press.\n* IPCC, 2019: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* Molinos, J.G., et al. (2016): Climate velocity and the future global redistribution of marine biodiversity, NATURE Climate Change 6 doi:10.10383/NCLIMATE2769.\n* Mulet S, Buongiorno Nardelli B, Good S, A. Pisano A, E. Greiner, Monier M, 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208.\n* Ram\u00edrez, F., I. Af\u00e1n, L.S. Davis, and A. Chiaradia (2017): Climate impacts on global hot spots of marine biodiversity. Science Advances 3, no. 2 : e1601198.\n* Sun, Y., Z. Zhong, T. Li, L. Yi, Y. Hu, H. Wan, H. Chen, Q. Liao, C. Ma and Q. Li, 2017: Impact of Ocean Warming on Tropical Cyclone Size and Its Destructiveness, Nature Scientific Reports, Volume 7 (8154), https://doi.org/10.1038/s41598-017-08533-6.\n* Trenberth, K. E., L. J. Cheng, P. Jacobs, Y. X. Zhang, and J. Fasullo (2018): Hurricane Harvey links to ocean heat content and climate change adaptation. Earth's Future, 6, 730--744, https://doi.org/10.1029/2018EF000825.\n", "doi": "10.48670/moi-00244", "instrument": null, "keywords": "change-over-time-in-sea-water-temperature,coastal-marine-environment,global-ocean,global-omi-tempsal-tyz-trend,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Zonal Mean Subsurface Temperature cumulative trend from Multi-Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nBased on daily, global climate sea surface temperature (SST) analyses generated by the European Space Agency (ESA) SST Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) (Merchant et al., 2019; product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). \nAnalysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: \n1.\tThe daily analyses were averaged to create monthly means. \n2.\tA climatology was calculated by averaging the monthly means over the period 1993 - 2014. \n3.\tMonthly anomalies were calculated by differencing the monthly means and the climatology. \n4.\tAn area averaged time series was calculated by averaging the monthly fields over the globe, with each grid cell weighted according to its area. \n5.\tThe time series was passed through the X11 seasonal adjustment procedure, which decomposes the time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. \n6.\tThe slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016).\n\n**CMEMS KEY FINDINGS**\n\nOver the period 1993 to 2021, the global average linear trend was 0.015 \u00b1 0.001\u00b0C / year (95% confidence interval). 2021 is nominally the sixth warmest year in the time series. Aside from this trend, variations in the time series can be seen which are associated with changes between El Ni\u00f1o and La Ni\u00f1a conditions. For example, peaks in the time series coincide with the strong El Ni\u00f1o events that occurred in 1997/1998 and 2015/2016 (Gasparin et al., 2018).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00242\n\n**References:**\n\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Gasparin, F., von Schuckmann, K., Desportes, C., Sathyendranath, S. and Pardo, S. 2018. El Ni\u00f1o southern oscillation. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* Good, S.A., Kennedy, J.J, and Embury, O. Global sea surface temperature anomalies in 2018 and historical changes since 1993. In: von Schuckmann et al. 2020, Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, doi: 10.1080/1755876X.2020.1785097.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\u202f\n* Mulet S., Nardelli B.B., Good S., Pisano A., Greiner E., Monier M., Autret E., Axell L., Boberg F., Ciliberti S. 2018. Ocean temperature and salinity. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* Pezzulli, S., Stephenson, D.B. and Hannachi A. 2005. The variability of seasonality. J Clim. 18: 71\u2013 88, doi: 10.1175/JCLI-3256.1.\n* Roquet H , Pisano A., Embury O. 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus marine environment monitoring service ocean state report. J Oper Ocean. 9(suppl. 2). doi:10.1080/1755876X.2016.1273446.\n* Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63: 1379\u2013 1389, doi: 10.1080/01621459.1968.10480934.\n", "doi": "10.48670/moi-00242", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-tempsal-sst-area-averaged-anomalies,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Sea Surface Temperature time series and trend from Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_sst_trend": {"abstract": "**DEFINITION**\n\nBased on daily, global climate sea surface temperature (SST) analyses generated by the European Space Agency (ESA) SST Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) (Merchant et al., 2019; product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). \nAnalysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: \n1.\tThe daily analyses were averaged to create monthly means. \n2.\tA climatology was calculated by averaging the monthly means over the period 1993 - 2014. \n3.\tMonthly anomalies were calculated by differencing the monthly means and the climatology. \n4.\tThe time series for each grid cell was passed through the X11 seasonal adjustment procedure, which decomposes a time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. \n5.\tThe slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016).\n\n**CMEMS KEY FINDINGS**\n\nWarming trends occurred over most of the globe between 1993 and 2021. One of the exceptions is the North Atlantic, which has a region south of Greenland where a cooling trend is found. The cooling in this area has been previously noted as occurring on centennial time scales (IPCC, 2013; Caesar et al., 2018; Sevellee et al., 2017).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00243\n\n**References:**\n\n* Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G. and Saba, V., 2018. Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature, 556(7700), p.191. DOI: 10.1038/s41586-018-0006-5.\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Good, S.A., Kennedy, J.J, and Embury, O. Global sea surface temperature anomalies in 2018 and historical changes since 1993. In: von Schuckmann et al. 2020, Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, doi: 10.1080/1755876X.2020.1785097.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\u202f\n* Mulet S., Nardelli B.B., Good S., Pisano A., Greiner E., Monier M., Autret E., Axell L., Boberg F., Ciliberti S. 2018. Ocean temperature and salinity. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.\n* Pezzulli, S., Stephenson, D.B. and Hannachi A. 2005. The variability of seasonality. J Clim. 18: 71\u2013 88, doi: 10.1175/JCLI-3256.1.\n* Roquet H , Pisano A., Embury O. 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus marine environment monitoring service ocean state report. J Oper Ocean. 9(suppl. 2). doi:10.1080/1755876X.2016.1273446.\n* Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63: 1379\u2013 1389, doi: 10.1080/01621459.1968.10480934.\n* S\u00e9vellec, F., Fedorov, A.V. and Liu, W., 2017. Arctic sea-ice decline weakens the Atlantic meridional overturning circulation. Nature Climate Change, 7(8), p.604, doi: 10.1038/nclimate3353.\n", "doi": "10.48670/moi-00243", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-tempsal-sst-trend,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Sea Surface Temperature trend map from Observations Reprocessing"}, "GLOBAL_OMI_WMHE_heattrp": {"abstract": "**DEFINITION**\n\nHeat transport across lines are obtained by integrating the heat fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models\u2019 daily output.\nThe mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW).\n\n**CONTEXT**\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\n**CMEMS KEY FINDINGS**\n\nThe mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00245\n\n**References:**\n\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n* Bricaud C, Drillet Y, Garric G. 2016. Ocean mass and heat transport. In CMEMS Ocean State Report, Journal of Operational Oceanography, 9, http://dx.doi.org/10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00245", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-wmhe-heattrp,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mean Heat Transport across sections from Reanalysis"}, "GLOBAL_OMI_WMHE_northward_mht": {"abstract": "**DEFINITION**\n\nMeridional Heat Transport is computed by integrating the heat fluxes along the zonal direction and from top to bottom of the ocean. \nThey are given over 3 basins (Global Ocean, Atlantic Ocean and Indian+Pacific Ocean) and for all the grid points in the meridional grid of each basin. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW).\n\n**CONTEXT**\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\n**CMEMS KEY FINDINGS**\n\nAfter an anusual 2016 year (Bricaud 2016), with a higher global meridional heat transport in the tropical band explained by, the increase of northward heat transport at 5-10 \u00b0 N in the Pacific Ocean during the El Ni\u00f1o event, 2017 northward heat transport is lower than the 1993-2014 reference value in the tropical band, for both Atlantic and Indian + Pacific Oceans. At the higher latitudes, 2017 northward heat transport is closed to 1993-2014 values.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00246\n\n**References:**\n\n* Crosnier L, Barnier B, Treguier AM, 2001. Aliasing inertial oscillations in a 1/6\u00b0 Atlantic circulation model: impact on the mean meridional heat transport. Ocean Modelling, vol 3, issues 1-2, pp21-31. https://doi.org/10.1016/S1463-5003(00)00015-9\n* Ganachaud, A. , Wunsch C. 2003. Large-Scale Ocean Heat and Freshwater Transports during the World Ocean Circulation Experiment. J. Climate, 16, 696\u2013705, https://doi.org/10.1175/1520-0442(2003)016<0696:LSOHAF>2.0.CO;2\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n", "doi": "10.48670/moi-00246", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-wmhe-northward-mht,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Northward Heat Transport for Global Ocean, Atlantic and Indian+Pacific basins from Reanalysis"}, "GLOBAL_OMI_WMHE_voltrp": {"abstract": "**DEFINITION**\n\nVolume transport across lines are obtained by integrating the volume fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models\u2019 daily output.\nThe mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in Sverdrup (Sv).\n\n**CONTEXT**\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\n**CMEMS KEY FINDINGS**\n\nThe mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. At Drake Passage, the multi-product approach (product no. 2.4.1) is larger than the value (130 Sv) of Lumpkin and Speer (2007), but smaller than the new observational based results of Colin de Verdi\u00e8re and Ollitrault, (2016) (175 Sv) and Donohue (2017) (173.3 Sv).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00247\n\n**References:**\n\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n", "doi": "10.48670/moi-00247", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-wmhe-voltrp,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mean Volume Transport across sections from Reanalysis"}, "IBI_ANALYSISFORECAST_BGC_005_004": {"abstract": "The IBI-MFC provides a high-resolution biogeochemical analysis and forecast product covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as daily averaged forecasts with a horizon of 10 days (updated on a weekly basis) are available on the catalogue.\nTo this aim, an online coupled physical-biogeochemical operational system is based on NEMO-PISCES at 1/36\u00b0 and adapted to the IBI area, being Mercator-Ocean in charge of the model code development. PISCES is a model of intermediate complexity, with 24 prognostic variables. It simulates marine biological productivity of the lower trophic levels and describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe).\nThe product provides daily and monthly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, surface partial pressure of carbon dioxide, zooplankton and light attenuation.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00026\n\n**References:**\n\n* Gutknecht, E. and Reffray, G. and Mignot, A. and Dabrowski, T. and Sotillo, M. G. Modelling the marine ecosystem of Iberia-Biscay-Ireland (IBI) European waters for CMEMS operational applications. Ocean Sci., 15, 1489\u20131516, 2019. https://doi.org/10.5194/os-15-1489-2019\n", "doi": "10.48670/moi-00026", "instrument": null, "keywords": "coastal-marine-environment,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,ibi-analysisforecast-bgc-005-004,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "IBI_ANALYSISFORECAST_PHY_005_001": {"abstract": "The IBI-MFC provides a high-resolution ocean analysis and forecast product (daily run by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as forecasts of different temporal resolutions with a horizon of 10 days (updated on a daily basis) are available on the catalogue.\nThe system is based on a eddy-resolving NEMO model application at 1/36\u00ba horizontal resolution, being Mercator-Ocean in charge of the model code development. The hydrodynamic forecast includes high frequency processes of paramount importance to characterize regional scale marine processes: tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge, wave forcing in forecast, etc. A weekly update of IBI downscaled analysis is also delivered as historic IBI best estimates.\nThe product offers 3D daily and monthly ocean fields, as well as hourly mean and 15-minute instantaneous values for some surface variables. Daily and monthly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are delivered. Doodson-filtered detided mean sea level and horizontal surface currents are also provided. Finally, 15-minute instantaneous values of Sea Surface Height and Currents are also given.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00027\n\n**References:**\n\n* Sotillo, M.G.; Campuzano, F.; Guihou, K.; Lorente, P.; Olmedo, E.; Matulka, A.; Santos, F.; Amo-Baladr\u00f3n, M.A.; Novellino, A. River Freshwater Contribution in Operational Ocean Models along the European Atlantic Fa\u00e7ade: Impact of a New River Discharge Forcing Data on the CMEMS IBI Regional Model Solution. J. Mar. Sci. Eng. 2021, 9, 401. https://doi.org/10.3390/jmse9040401\n* Mason, E. and Ruiz, S. and Bourdalle-Badie, R. and Reffray, G. and Garc\u00eda-Sotillo, M. and Pascual, A. New insight into 3-D mesoscale eddy properties from CMEMS operational models in the western Mediterranean. Ocean Sci., 15, 1111\u20131131, 2019. https://doi.org/10.5194/os-15-1111-2019\n* Lorente, P. and Garc\u00eda-Sotillo, M. and Amo-Baladr\u00f3n, A. and Aznar, R. and Levier, B. and S\u00e1nchez-Garrido, J. C. and Sammartino, S. and de Pascual-Collar, \u00c1. and Reffray, G. and Toledano, C. and \u00c1lvarez-Fanjul, E. Skill assessment of global, regional, and coastal circulation forecast models: evaluating the benefits of dynamical downscaling in IBI (Iberia-Biscay-Ireland) surface waters. Ocean Sci., 15, 967\u2013996, 2019. https://doi.org/10.5194/os-15-967-2019\n* Aznar, R., Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Amo-Baladr\u00f3n, A., Reffray, G., and Alvarez Fanjul, E. Strengths and weaknesses of the CMEMS forecasted and reanalyzed solutions for the Iberia-Biscay-Ireland (IBI) waters. J. Mar. Syst., 159, 1\u201314, https://doi.org/10.1016/j.jmarsys.2016.02.007, 2016\n* Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Reffray, G., Amo-Baladr\u00f3n, A., Benkiran, M., and Alvarez Fanjul, E.: The MyOcean IBI Ocean Forecast and Reanalysis Systems: operational products and roadmap to the future Copernicus Service, J. Oper. Oceanogr., 8, 63\u201379, https://doi.org/10.1080/1755876X.2015.1014663, 2015.\n", "doi": "10.48670/moi-00027", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,forecast,iberian-biscay-irish-seas,ibi-analysisforecast-phy-005-001,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "IBI_ANALYSISFORECAST_WAV_005_005": {"abstract": "The IBI-MFC provides a high-resolution wave analysis and forecast product (run twice a day by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates), as well as hourly instantaneous forecasts with a horizon of up to 10 days (updated on a daily basis) are available on the catalogue.\nThe IBI wave model system is based on the MFWAM model and runs on a grid of 1/36\u00ba of horizontal resolution forced with the ECMWF hourly wind data. The system assimilates significant wave height (SWH) altimeter data and CFOSAT wave spectral data (supplied by M\u00e9t\u00e9o-France), and it is forced by currents provided by the IBI ocean circulation system. \nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Additionally, the IBI wave system is set up to provide internally some key parameters adequate to be used as forcing in the IBI NEMO ocean model forecast run.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00025\n\n**References:**\n\n* Toledano, C.; Ghantous, M.; Lorente, P.; Dalphinet, A.; Aouf, L.; Sotillo, M.G. Impacts of an Altimetric Wave Data Assimilation Scheme and Currents-Wave Coupling in an Operational Wave System: The New Copernicus Marine IBI Wave Forecast Service. J. Mar. Sci. Eng. 2022, 10, 457. https://doi.org/10.3390/jmse10040457\n", "doi": "10.48670/moi-00025", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,forecast,iberian-biscay-irish-seas,ibi-analysisforecast-wav-005-005,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),wave-spectra,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-11-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast"}, "IBI_MULTIYEAR_BGC_005_003": {"abstract": "The IBI-MFC provides a biogeochemical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources.\nTo this aim, an application of the biogeochemical model PISCES is run simultaneously with the ocean physical IBI reanalysis, generating both products at the same 1/12\u00b0 horizontal resolution. The PISCES model is able to simulate the first levels of the marine food web, from nutrients up to mesozooplankton and it has 24 state variables.\nThe product provides daily, monthly and yearly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, zooplankton and surface partial pressure of carbon dioxide. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered.\nFor all the abovementioned variables new interim datasets will be provided to cover period till month - 4.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00028\n\n**References:**\n\n* Aznar, R., Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Amo-Baladr\u00f3n, A., Reffray, G., and Alvarez Fanjul, E. Strengths and weaknesses of the CMEMS forecasted and reanalyzed solutions for the Iberia-Biscay-Ireland (IBI) waters. J. Mar. Syst., 159, 1\u201314, https://doi.org/10.1016/j.jmarsys.2016.02.007, 2016\n", "doi": "10.48670/moi-00028", "instrument": null, "keywords": "coastal-marine-environment,euphotic-zone-depth,iberian-biscay-irish-seas,ibi-multiyear-bgc-005-003,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-08-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "IBI_MULTIYEAR_PHY_005_002": {"abstract": "The IBI-MFC provides a ocean physical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources. \nThe IBI model numerical core is based on the NEMO v3.6 ocean general circulation model run at 1/12\u00b0 horizontal resolution. Altimeter data, in situ temperature and salinity vertical profiles and satellite sea surface temperature are assimilated.\nThe product offers 3D daily, monthly and yearly ocean fields, as well as hourly mean fields for surface variables. Daily, monthly and yearly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are distributed. Besides, daily means of air-sea fluxes are provided. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered. For all the abovementioned variables new interim datasets will be provided to cover period till month - 4.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00029", "doi": "10.48670/moi-00029", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,iberian-biscay-irish-seas,ibi-multiyear-phy-005-002,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-08-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "IBI_MULTIYEAR_WAV_005_006": {"abstract": "The IBI-MFC provides a high-resolution wave reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1980 and being regularly extended on a yearly basis. The model system is run by Nologin with the support of CESGA in terms of supercomputing resources. \nThe Multi-Year model configuration is based on the MFWAM model developed by M\u00e9t\u00e9o-France (MF), covering the same region as the IBI-MFC Near Real Time (NRT) analysis and forecasting product and with the same horizontal resolution (1/36\u00ba). The system assimilates significant wave height (SWH) altimeter data and wave spectral data (Envisat and CFOSAT), supplied by MF. Both, the MY and the NRT products, are fed by ECMWF hourly winds. Specifically, the MY system is forced by the ERA5 reanalysis wind data. As boundary conditions, the NRT system uses the 2D wave spectra from the Copernicus Marine GLOBAL forecast system, whereas the MY system is nested to the GLOBAL reanalysis.\nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Besides, air-sea fluxes are provided. Additionally, climatological parameters of significant wave height (VHM0) and zero -crossing wave period (VTM02) are delivered for the time interval 1993-2016.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00030", "doi": "10.48670/moi-00030", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,iberian-biscay-irish-seas,ibi-multiyear-wav-005-006,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "IBI_OMI_CURRENTS_cui": {"abstract": "**DEFINITION**\n\nThe Coastal Upwelling Index (CUI) is computed along the African and the Iberian Peninsula coasts. For each latitudinal point from 27\u00b0N to 42\u00b0N the Coastal Upwelling Index is defined as the temperature difference between the maximum and minimum temperature in a range of distance from the coast up to 3.5\u00ba westwards.\n\u3016CUI\u3017_lat=max\u2061(T_lat )-min\u2061(T_lat)\nA high Coastal Upwelling Index indicates intense upwelling conditions.\nThe index is computed from the following Copernicus Marine products:\n\tIBI-MYP: IBI_MULTIYEAR_PHY_005_002 (1993-2019)\n\tIBI-NRT: IBI_ANALYSISFORECAST_PHYS_005_001 (2020 onwards)\n\n**CONTEXT**\n\nCoastal upwelling process occurs along coastlines as the result of deflection of the oceanic water away from the shore. Such deflection is produced by Ekman transport induced by persistent winds parallel to the coastline (Sverdrup, 1938). When this transported water is forced, the mass balance is maintained by pumping of ascending intermediate water. This water is typically denser, cooler and richer in nutrients. The Iberia-Biscay-Ireland domain contains two well-documented Eastern Boundary Upwelling Ecosystems, they are hosted under the same system known as Canary Current Upwelling System (Fraga, 1981; Hempel, 1982). This system is one of the major coastal upwelling regions of the world and it is produced by the eastern closure of the Subtropical Gyre. The North West African (NWA) coast presents an intense upwelling region that extends from Morocco to south of Senegal, likewise the western coast of the Iberian Peninsula (IP) shows a seasonal upwelling behavior. These two upwelling domains are separated by the presence of the Gulf of Cadiz, where the coastline does not allow the formation of upwelling conditions from 34\u00baN up to 37\u00baN.\nThe Copernicus Marine Service Coastal Upwelling Index is defined following the steps of several previous upwelling indices described in literature. More details and full scientific evaluation can be found in the dedicated section in the first issue of the Copernicus Marine Service Ocean State report (Sotillo et al., 2016).\n\n**CMEMS KEY FINDINGS**\n\nThe NWA coast (latitudes below 34\u00baN) shows a quite constantlow variability of the periodicity and the intensity of the upwelling, few periods of upwelling intensifications are found in years 1993-1995, and 2003-2004.\nIn the IP coast (latitudes higher than 37\u00baN) the interannual variability is more remarkable showing years with high upwelling activity (1994, 2004, and 2015-2017) followed by periods of lower activity (1996-1998, 2003, 2011, and 2013).\nAccording to the results of the IBI-NRT system, 2020 was a year with weak upwelling in the IP latitudes. \nWhile in the NWA coast the upwelling activity was more intense than the average.\nThe analysis of trends in the period 1993-2019 shows significant positive trend of the upwelling index in the IP latitudes. This trend implies an increase of temperature differences between the coastal and offshore waters of approximately 0.02 \u00b0C/Year.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00248\n\n**References:**\n\n* Fraga F. 1981. Upwelling off the Galician Coast, Northwest Spain. In: Richardson FA, editor. Coastal Upwelling. Washington (DC): Am Geoph Union; p. 176\u2013182.\n* Hempel G. 1982. The Canary current: studies of an upwelling system. Introduction. Rapp. Proc. Reun. Cons. Int. Expl. Mer., 180, 7\u20138.\n* Sotillo MG, Levier B, Pascual A, Gonzalez A. 2016 Iberian-Biscay-Irish Sea. In von Scuckmann et al. (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* Sverdrup HV. 1938. On the process of upwelling. J Mar Res. 1:155\u2013164.\n", "doi": "10.48670/moi-00248", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-currents-cui,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Coastal Upwelling Index from Reanalysis"}, "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS IBI_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_WAV_005_006) and the Analysis product (IBI_ANALYSIS_FORECAST_WAV_005_005).\nTwo parameters have been considered for this OMI:\n\u2022\tMap of the 99th mean percentile: It is obtained from the Multi-Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2021).\n\u2022\tAnomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2022.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe sea state and its related spatio-temporal variability affect dramatically maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions.\nThe Iberia-Biscay-Ireland region, which covers the North-East Atlantic Ocean from Canary Islands to Ireland, is characterized by two different sea state wave climate regions: whereas the northern half, impacted by the North Atlantic subpolar front, is of one of the world\u2019s greatest wave generating regions (M\u00f8rk et al., 2010; Folley, 2017), the southern half, located at subtropical latitudes, is by contrast influenced by persistent trade winds and thus by constant and moderate wave regimes.\nThe North Atlantic Oscillation (NAO), which refers to changes in the atmospheric sea level pressure difference between the Azores and Iceland, is a significant driver of wave climate variability in the Northern Hemisphere. The influence of North Atlantic Oscillation on waves along the Atlantic coast of Europe is particularly strong in and has a major impact on northern latitudes wintertime (Mart\u00ednez-Asensio et al. 2016; Bacon and Carter, 1991; Bouws et al., 1996; Bauer, 2001; Wolf et al., 2002; Gleeson et al., 2017). Swings in the North Atlantic Oscillation index produce changes in the storms track and subsequently in the wind speed and direction over the Atlantic that alter the wave regime. When North Atlantic Oscillation index is in its positive phase, storms usually track northeast of Europe and enhanced westerly winds induce higher than average waves in the northernmost Atlantic Ocean. Conversely, in the negative North Atlantic Oscillation phase, the track of the storms is more zonal and south than usual, with trade winds (mid latitude westerlies) being slower and producing higher than average waves in southern latitudes (Marshall et al., 2001; Wolf et al., 2002; Wolf and Woolf, 2006). \nAdditionally a variety of previous studies have uniquevocally determined the relationship between the sea state variability in the IBI region and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Mart\u00ednez-Asensio et al., 2016). \nIn this context, long\u2010term statistical analysis of reanalyzed model data is mandatory not only to disentangle other driving agents of wave climate but also to attempt inferring any potential trend in the number and/or intensity of extreme wave events in coastal areas with subsequent socio-economic and environmental consequences.\n\n**CMEMS KEY FINDINGS**\n\nThe climatic mean of 99th percentile (1993-2021) reveals a north-south gradient of Significant Wave Height with the highest values in northern latitudes (above 8m) and lowest values (2-3 m) detected southeastward of Canary Islands, in the seas between Canary Islands and the African Continental Shelf. This north-south pattern is the result of the two climatic conditions prevailing in the region and previously described.\nThe 99th percentile anomalies in 2023 show that during this period, the central latitudes of the domain (between 37 \u00baN and 50 \u00baN) were affected by extreme wave events that exceeded up to twice the standard deviation of the anomalies. These events impacted not only the open waters of the Northeastern Atlantic but also European coastal areas such as the west coast of Portugal, the Spanish Atlantic coast, and the French coast, including the English Channel.\nAdditionally, the impact of significant wave extremes exceeding twice the standard deviation of anomalies was detected in the Mediterranean region of the Balearic Sea and the Algerian Basin. This pattern is commonly associated with the impact of intense Tramontana winds originating from storms that cross the Iberian Peninsula from the Gulf of Biscay.\n\n**Figure caption**\n\nIberia-Biscay-Ireland Significant Wave Height extreme variability: Map of the 99th mean percentile computed from the Multi Year Product (left panel) and anomaly of the 99th percentile in 2022 computed from the Analysis product (right panel). Transparent grey areas (if any) represent regions where anomaly exceeds the climatic standard deviation (light grey) and twice the climatic standard deviation (dark grey).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00249\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Bacon S, Carter D J T. 1991. Wave climate changes in the north Atlantic and North Sea, International Journal of Climatology, 11, 545\u2013558.\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Bouws E, Jannink D, Komen GJ. 1996. The increasing wave height in the North Atlantic Ocean, Bull. Am. Met. Soc., 77, 2275\u20132277.\n* Folley M. 2017. The wave energy resource. In Pecher A, Kofoed JP (ed.), Handbook of Ocean Wave Energy, Ocean Engineering & Oceanography 7, doi:10.1007/978-3-319-39889-1_3.\n* Gleeson E, Gallagher S, Clancy C, Dias F. 2017. NAO and extreme ocean states in the Northeast Atlantic Ocean, Adv. Sci. Res., 14, 23\u201333, doi:10.5194/asr-14-23-2017.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2015. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2015 In book: .Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* Izaguirre C, M\u00e9ndez F J, Men\u00e9ndez M, Losada I J. 2011. Global extreme wave height variability based on satellite data Cristina. Geoph. Res. Letters, Vol. 38, L10607, doi: 10.1029/2011GL047302.\n* Mart\u00ednez-Asensio A, Tsimplis M N, Marcos M, Feng F, Gomis D, Jord\u00e0a G, Josey S A. 2016. Response of the North Atlantic wave climate to atmospheric modes of variability. International Journal of Climatology, 36, 1210\u20131225, doi: 10.1002/joc.4415.\n* M\u00f8rk G, Barstow S, Kabush A, Pontes MT. 2010. Assessing the global wave energy potential. Proceedings of OMAE2010 29th International Conference on Ocean, Offshore Mechanics and Arctic Engineering June 6-11, 2010, Shanghai, China.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Woolf D K, Challenor P G, Cotton P D. 2002. Variability and predictability of the North Atlantic wave climate, J. Geophys. Res., 107(C10), 3145, doi:10.1029/2001JC001124.\n* Wolf J, Woolf D K. 2006. Waves and climate change in the north-east Atlantic. Geophysical Research Letters, Vol. 33, L06604, doi: 10.1029/2005GL025113.\n* Young I R, Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548-552, doi: 10.1126/science.aav9527.\n* Kushnir Y, Cardone VJ, Greenwood JG, Cane MA. 1997. The recent increase in North Atlantic wave heights. Journal of Climate 10:2107\u20132113.\n* Marshall, J., Kushnir, Y., Battisti, D., Chang, P., Czaja, A., Dickson, R., ... & Visbeck, M. (2001). North Atlantic climate variability: phenomena, impacts and mechanisms. International Journal of Climatology: A Journal of the Royal Meteorological Society, 21(15), 1863-1898.\n", "doi": "10.48670/moi-00249", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-seastate-extreme-var-swh-mean-and-anomaly,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Significant Wave Height extreme from Reanalysis"}, "IBI_OMI_SEASTATE_swi": {"abstract": "**DEFINITION**\n\nThe Strong Wave Incidence index is proposed to quantify the variability of strong wave conditions in the Iberia-Biscay-Ireland regional seas. The anomaly of exceeding a threshold of Significant Wave Height is used to characterize the wave behavior. A sensitivity test of the threshold has been performed evaluating the differences using several ones (percentiles 75, 80, 85, 90, and 95). From this indicator, it has been chosen the 90th percentile as the most representative, coinciding with the state-of-the-art.\nTwo CMEMS products are used to compute the Strong Wave Incidence index:\n\u2022\tIBI-WAV-MYP: IBI_REANALYSIS_WAV_005_006\n\u2022\tIBI-WAV-NRT: IBI_ANALYSIS_FORECAST_WAV_005_005\nThe Strong Wave Incidence index (SWI) is defined as the difference between the climatic frequency of exceedance (Fclim) and the observational frequency of exceedance (Fobs) of the threshold defined by the 90th percentile (ThP90) of Significant Wave Height (SWH) computed on a monthly basis from hourly data of IBI-WAV-MYP product:\nSWI = Fobs(SWH > ThP90) \u2013 Fclim(SWH > ThP90)\nSince the Strong Wave Incidence index is defined as a difference of a climatic mean and an observed value, it can be considered an anomaly. Such index represents the percentage that the stormy conditions have occurred above/below the climatic average. Thus, positive/negative values indicate the percentage of hourly data that exceed the threshold above/below the climatic average, respectively.\n\n**CONTEXT**\n\nOcean waves have a high relevance over the coastal ecosystems and human activities. Extreme wave events can entail severe impacts over human infrastructures and coastal dynamics. However, the incidence of severe (90th percentile) wave events also have valuable relevance affecting the development of human activities and coastal environments. The Strong Wave Incidence index based on the CMEMS regional analysis and reanalysis product provides information on the frequency of severe wave events.\nThe IBI-MFC covers the Europe\u2019s Atlantic coast in a region bounded by the 26\u00baN and 56\u00baN parallels, and the 19\u00baW and 5\u00baE meridians. The western European coast is located at the end of the long fetch of the subpolar North Atlantic (M\u00f8rk et al., 2010), one of the world\u2019s greatest wave generating regions (Folley, 2017). Several studies have analyzed changes of the ocean wave variability in the North Atlantic Ocean (Bacon and Carter, 1991; Kursnir et al., 1997; WASA Group, 1998; Bauer, 2001; Wang and Swail, 2004; Dupuis et al., 2006; Wolf and Woolf, 2006; Dodet et al., 2010; Young et al., 2011; Young and Ribal, 2019). The observed variability is composed of fluctuations ranging from the weather scale to the seasonal scale, together with long-term fluctuations on interannual to decadal scales associated with large-scale climate oscillations. Since the ocean surface state is mainly driven by wind stresses, part of this variability in Iberia-Biscay-Ireland region is connected to the North Atlantic Oscillation (NAO) index (Bacon and Carter, 1991; Hurrell, 1995; Bouws et al., 1996, Bauer, 2001; Woolf et al., 2002; Tsimplis et al., 2005; Gleeson et al., 2017). However, later studies have quantified the relationships between the wave climate and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation pattern, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Mat\u00ednez-Asensio et al., 2016).\nThe Strong Wave Incidence index provides information on incidence of stormy events in four monitoring regions in the IBI domain. The selected monitoring regions (Figure 1.A) are aimed to provide a summarized view of the diverse climatic conditions in the IBI regional domain: Wav1 region monitors the influence of stormy conditions in the West coast of Iberian Peninsula, Wav2 region is devoted to monitor the variability of stormy conditions in the Bay of Biscay, Wav3 region is focused in the northern half of IBI domain, this region is strongly affected by the storms transported by the subpolar front, and Wav4 is focused in the influence of marine storms in the North-East African Coast, the Gulf of Cadiz and Canary Islands.\nMore details and a full scientific evaluation can be found in the CMEMS Ocean State report (Pascual et al., 2020).\n\n**CMEMS KEY FINDINGS**\n\nThe analysis of the index in the last decades do not show significant trends of the strong wave conditions over the period 1992-2021 with 99% confidence. The maximum wave event reported in region WAV1 (B) occurred in February 2014, producing an increment of 25% of strong wave conditions in the region. Two maximum wave events are found in WAV2 (C) with an increment of 15% of high wave conditions in November 2009 and February 2014. As in regions WAV1 and WAV2, in the region WAV3 (D), a strong wave event took place in February 2014, this event is one of the maximum events reported in the region with an increment of strong wave conditions of 20%, two months before (December 2013) there was a storm of similar characteristics affecting this region, other events of similar magnitude are detected on October 2000 and November 2009. The region WAV4 (E) present its maximum wave event in January 1996, such event produced a 25% of increment of strong wave conditions in the region. Despite of each monitoring region is affected by independent wave events; the analysis shows several past higher-than-average wave events that were propagated though several monitoring regions: November-December 2010 (WAV3 and WAV2); February 2014 (WAV1, WAV2, and WAV3); and February-March 2018 (WAV1 and WAV4).\nThe analysis of the NRT period (January 2022 onwards) depicts a significant event that occurred in November 2022, which affected the WAV2 and WAV3 regions, resulting in a 15% and 25% increase in maximum wave conditions, respectively. In the case of the WAV3 region, this event was the strongest event recorded in this region.\nIn the WAV4 region, an event that occurred in February 2024 was the second most intense on record, showing an 18% increase in strong wave conditions in the region.\nIn the WAV1 region, the NRT period includes two high-intensity events that occurred in February 2024 (21% increase in strong wave conditions) and April 2022 (18% increase in maximum wave conditions).\n\n**Figure caption**\n\n(A) Mean 90th percentile of Sea Wave Height computed from IBI_REANALYSIS_WAV_005_006 product at an hourly basis. Gray dotted lines denote the four monitoring areas where the Strong Wave Incidence index is computed. (B, C, D, and E) Strong Wave Incidence index averaged in monitoring regions WAV1 (A), WAV2 (B), WAV3 (C), and WAV4 (D). Panels show merged results of two CMEMS products: IBI_REANALYSIS_WAV_005_006 (blue), IBI_ANALYSIS_FORECAST_WAV_005_005 (orange). The trend and 99% confidence interval of IBI-MYP product is included (bottom right).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00251\n\n**References:**\n\n* Bacon S, Carter D J T. 1991. Wave climate changes in the north Atlantic and North Sea, International Journal of Climatology, 11, 545\u2013558.\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Bouws E, Jannink D, Komen GJ. 1996. The increasing wave height in the North Atlantic Ocean, Bull. Am. Met. Soc., 77, 2275\u20132277.\n* Dodet G, Bertin X, Taborda R. 2010. Wave climate variability in the North-East Atlantic Ocean over the last six decades, Ocean Modelling, 31, 120\u2013131.\n* Dupuis H, Michel D, Sottolichio A. 2006. Wave climate evolution in the Bay of Biscay over two decades. Journal of Marine Systems, 63, 105\u2013114.\n* Folley M. 2017. The wave energy resource. In Pecher A, Kofoed JP (ed.), Handbook of Ocean Wave Energy, Ocean Engineering & Oceanography 7, doi:10.1007/978-3-319-39889-1_3.\n* Gleeson E, Gallagher S, Clancy C, Dias F. 2017. NAO and extreme ocean states in the Northeast Atlantic Ocean, Adv. Sci. Res., 14, 23\u201333, doi:10.5194/asr-14-23-2017.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hurrell JW. 1995. Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation, Science, 269:676\u2013679.\n* Izaguirre C, M\u00e9ndez F J, Men\u00e9ndez M, Losada I J. 2011. Global extreme wave height variability based on satellite data Cristina. Geoph. Res. Letters, Vol. 38, L10607, doi: 10.1029/2011GL047302.\n* Kushnir Y, Cardone VJ, Greenwood JG, Cane MA. 1997. The recent increase in North Atlantic wave heights. Journal of Climate 10:2107\u20132113.\n* Mart\u00ednez-Asensio A, Tsimplis M N, Marcos M, Feng F, Gomis D, Jord\u00e0a G, Josey S A. 2016. Response of the North Atlantic wave climate to atmospheric modes of variability. International Journal of Climatology, 36, 1210\u20131225, doi: 10.1002/joc.4415.\n* M\u00f8rk G, Barstow S, Kabush A, Pontes MT. 2010. Assessing the global wave energy potential. Proceedings of OMAE2010 29th International Conference on Ocean, Offshore Mechanics and Arctic Engineering June 6-11, 2010, Shanghai, China.\n* Pascual A., Levier B., Aznar R., Toledano C., Garc\u00eda-Valdecasas JM., Garc\u00eda M., Sotillo M., Aouf L., \u00c1lvarez E. (2020) Monitoring of wave sea state in the Iberia-Biscay-Ireland regional seas. In von Scuckmann et al. (2020) Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, DOI: 10.1080/1755876X.2020.1785097\n* Tsimplis M N, Woolf D K, Osborn T J, Wakelin S, Wolf J, Flather R, Shaw A G P, Woodworth P, Challenor P, Blackman D, Pert F, Yan Z, Jevrejeva S. 2005. Towards a vulnerability assessment of the UK and northern European coasts: the role of regional climate variability. Phil. Trans. R. Soc. A, Vol. 363, 1329\u20131358 doi:10.1098/rsta.2005.1571.\n* Wang X, Swail V. 2004. Historical and possible future changes of wave heights in northern hemisphere oceans. In: Perrie W (ed), Atmosphere ocean interactions, vol 2. Wessex Institute of Technology Press, Ashurst.\n* WASA-Group. 1998. Changing waves and storms in the Northeast Atlantic?, Bull. Am. Meteorol. Soc., 79:741\u2013760.\n* Wolf J, Woolf D K. 2006. Waves and climate change in the north-east Atlantic. Geophysical Research Letters, Vol. 33, L06604, doi: 10.1029/2005GL025113.\n* Woolf D K, Challenor P G, Cotton P D. 2002. Variability and predictability of the North Atlantic wave climate, J. Geophys. Res., 107(C10), 3145, doi:10.1029/2001JC001124.\n* Young I R, Zieger S, Babanin A V. 2011. Global Trends in Wind Speed and Wave Height. Science, Vol. 332, Issue 6028, 451-455, doi: 10.1126/science.1197219.\n* Young I R, Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548-552, doi: 10.1126/science.aav9527.\n", "doi": "10.48670/moi-00251", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-seastate-swi,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Strong Wave Incidence index from Reanalysis"}, "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS IBI_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_PHY_005_002) and the Analysis product (IBI_ANALYSISFORECAST_PHY_005_001).\nTwo parameters have been considered for this OMI:\n\u2022\tMap of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2021).\n\u2022\tAnomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2022 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe Sea Surface Temperature is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018).\n\nThe IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The Sea Surface Temperature field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the Iberia-Biscay-Ireland region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003).\nAlso relevant in the region are the upwelling processes taking place in the coastal margins. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980).\n\n**CMEMS KEY FINDINGS**\n\nIn the IBI region, the 99th mean percentile for 1993-2021 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in \u00c1lvarez Fanjul (2019) for the period 1993-2016.\nThe analysis of the 99th percentile anomaly in the year 2023 shows that this period has been affected by a severe impact of maximum SST values. Anomalies exceeding the standard deviation affect almost the entire IBI domain, and regions impacted by thermal anomalies surpassing twice the standard deviation are also widespread below the 43\u00baN parallel.\nExtreme SST values exceeding twice the standard deviation affect not only the open ocean waters but also the easter boundary upwelling areas such as the northern half of Portugal, the Spanish Atlantic coast up to Cape Ortegal, and the African coast south of Cape Aguer.\nIt is worth noting the impact of anomalies that exceed twice the standard deviation is widespread throughout the entire Mediterranean region included in this analysis.\n\n**Figure caption**\n\nIberia-Biscay-Ireland Surface Temperature extreme variability: Map of the 99th mean percentile computed from the Multi Year Product (left panel) and anomaly of the 99th percentile in 2022 computed from the Analysis product (right panel). Transparent grey areas (if any) represent regions where anomaly exceeds the climatic standard deviation (light grey) and twice the climatic standard deviation (dark grey).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00254\n\n**References:**\n\n* Alvarez I, Gomez-Gesteira M, DeCastro M, Lorenzo MN, Crespo AJC, Dias JM. 2011. Comparative analysis of upwelling influence between the western and northern coast of the Iberian Peninsula. Continental Shelf Research, 31(5), 388-399.\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Czaja A, Frankignoul C. 2002. Observed impact of Atlantic SST anomalies on the North Atlantic Oscillation. Journal of Climate, 15(6), 606-623.\n* Dickson RR, Gurbutt PA, Pillai VN. 1980. Satellite evidence of enhanced upwelling along the European continental slope. Journal of Physical Oceanography, 10(5), 813-819.\n* Dubois C, von Schuckmann K, Josey S. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 2.9, s66\u2013s70, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Edwards A, Jones K, Graham JM, Griffiths CR, MacDougall N, Patching J, Raine R. 1996. Transient coastal upwelling and water circulation in Bantry Bay, a ria on the south-west coast of Ireland. Estuarine, Coastal and Shelf Science, 42(2), 213-230.\n* Flatau MK, Talley L, Niiler PP. 2003. The North Atlantic Oscillation, surface current velocities, and SST changes in the subpolar North Atlantic. Journal of Climate, 16(14), 2355-2369.\n* Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Br\u00f6nnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2013. Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 1.1, s5\u2013s13, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sotillo MG, Levier B, Pascual A, Gonzalez A. 2016. Iberian-Biscay-Irish Sea. In von Schuckmann et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report No.1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00254", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-tempsal-extreme-var-temp-mean-and-anomaly,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Sea Surface Temperature extreme from Reanalysis"}, "IBI_OMI_WMHE_mow": {"abstract": "**DEFINITION**\n\nVariations of the Mediterranean Outflow Water at 1000 m depth are monitored through area-averaged salinity anomalies in specifically defined boxes. The salinity data are extracted from several CMEMS products and averaged in the corresponding monitoring domain: \n* IBI-MYP: IBI_MULTIYEAR_PHY_005_002\n* IBI-NRT: IBI_ANALYSISFORECAST_PHYS_005_001\n* GLO-MYP: GLOBAL_REANALYSIS_PHY_001_030\n* CORA: INSITU_GLO_TS_REP_OBSERVATIONS_013_002_b\n* ARMOR: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012\n\nThe anomalies of salinity have been computed relative to the monthly climatology obtained from IBI-MYP. Outcomes from diverse products are combined to deliver a unique multi-product result. Multi-year products (IBI-MYP, GLO,MYP, CORA, and ARMOR) are used to show an ensemble mean and the standard deviation of members in the covered period. The IBI-NRT short-range product is not included in the ensemble, but used to provide the deterministic analysis of salinity anomalies in the most recent year.\n\n**CONTEXT**\n\nThe Mediterranean Outflow Water is a saline and warm water mass generated from the mixing processes of the North Atlantic Central Water and the Mediterranean waters overflowing the Gibraltar sill (Daniault et al., 1994). The resulting water mass is accumulated in an area west of the Iberian Peninsula (Daniault et al., 1994) and spreads into the North Atlantic following advective pathways (Holliday et al. 2003; Lozier and Stewart 2008, de Pascual-Collar et al., 2019).\nThe importance of the heat and salt transport promoted by the Mediterranean Outflow Water flow has implications beyond the boundaries of the Iberia-Biscay-Ireland domain (Reid 1979, Paillet et al. 1998, van Aken 2000). For example, (i) it contributes substantially to the salinity of the Norwegian Current (Reid 1979), (ii) the mixing processes with the Labrador Sea Water promotes a salt transport into the inner North Atlantic (Talley and MacCartney, 1982; van Aken, 2000), and (iii) the deep anti-cyclonic Meddies developed in the African slope is a cause of the large-scale westward penetration of Mediterranean salt (Iorga and Lozier, 1999).\nSeveral studies have demonstrated that the core of Mediterranean Outflow Water is affected by inter-annual variability. This variability is mainly caused by a shift of the MOW dominant northward-westward pathways (Bozec et al. 2011), it is correlated with the North Atlantic Oscillation (Bozec et al. 2011) and leads to the displacement of the boundaries of the water core (de Pascual-Collar et al., 2019). The variability of the advective pathways of MOW is an oceanographic process that conditions the destination of the Mediterranean salt transport in the North Atlantic. Therefore, monitoring the Mediterranean Outflow Water variability becomes decisive to have a proper understanding of the climate system and its evolution (e.g. Bozec et al. 2011, Pascual-Collar et al. 2019).\nThe CMEMS IBI-OMI_WMHE_mow product is aimed to monitor the inter-annual variability of the Mediterranean Outflow Water in the North Atlantic. The objective is the establishment of a long-term monitoring program to observe the variability and trends of the Mediterranean water mass in the IBI regional seas. To do that, the salinity anomaly is monitored in key areas selected to represent the main reservoir and the three main advective spreading pathways. More details and a full scientific evaluation can be found in the CMEMS Ocean State report Pascual et al., 2018 and de Pascual-Collar et al. 2019.\n\n**CMEMS KEY FINDINGS**\n\nThe absence of long-term trends in the monitoring domain Reservoir (b) suggests the steadiness of water mass properties involved on the formation of Mediterranean Outflow Water.\nResults obtained in monitoring box North (c) present an alternance of periods with positive and negative anomalies. The last negative period started in 2016 reaching up to the present. Such negative events are linked to the decrease of the northward pathway of Mediterranean Outflow Water (Bozec et al., 2011), which appears to return to steady conditions in 2020 and 2021. \nResults for box West (d) reveal a cycle of negative (2015-2017) and positive (2017 up to the present) anomalies. The positive anomalies of salinity in this region are correlated with an increase of the westward transport of salinity into the inner North Atlantic (de Pascual-Collar et al., 2019), which appear to be maintained for years 2020-2021.\nResults in monitoring boxes North and West are consistent with independent studies (Bozec et al., 2011; and de Pascual-Collar et al., 2019), suggesting a westward displacement of Mediterranean Outflow Water and the consequent contraction of the northern boundary.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00258\n\n**References:**\n\n* Bozec A, Lozier MS, Chassignet EP, Halliwell GR. 2011. On the variability of the Mediterranean outflow water in the North Atlantic from 1948 to 2006. J Geophys Res 116:C09033. doi:10.1029/2011JC007191.\n* Daniault N, Maze JP, Arhan M. 1994. Circulation and mixing of MediterraneanWater west of the Iberian Peninsula. Deep Sea Res. Part I. 41:1685\u20131714.\n* de Pascual-Collar A, Sotillo MG, Levier B, Aznar R, Lorente P, Amo-Baladr\u00f3n A, \u00c1lvarez-Fanjul E. 2019. Regional circulation patterns of Mediterranean Outflow Water near the Iberian and African continental slopes. Ocean Sci., 15, 565\u2013582. https://doi.org/10.5194/os-15-565-2019.\n* Holliday NP. 2003. Air-sea interaction and circulation changes in the northeast Atlantic. J Geophys Res. 108(C8):3259. doi:10.1029/2002JC001344.\n* Iorga MC, Lozier MS. 1999. Signatures of the Mediterranean outflow from a North Atlantic climatology: 1. Salinity and density fields. Journal of Geophysical Research: Oceans, 104(C11), 25985-26009.\n* Lozier MS, Stewart NM. 2008. On the temporally varying northward penetration of Mediterranean overflow water and eastward penetration of Labrador Sea water. J Phys Oceanogr. 38(9):2097\u20132103. doi:10.1175/2008JPO3908.1.\n* Paillet J, Arhan M, McCartney M. 1998. Spreading of labrador Sea water in the eastern North Atlantic. J Geophys Res. 103 (C5):10223\u201310239.\n* Pascual A, Levier B, Sotillo M, Verbrugge N, Aznar R, Le Cann B. 2018. Characterisation of Mediterranean outflow w\u00e1ter in the Iberia-Gulf of Biscay-Ireland region. In: von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Braseur, P., Fennel, K., Djavidnia, S.: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11, sup1, s1-s142, doi:10.1080/1755876X.2018.1489208, 2018.\n* Reid JL. 1979. On the contribution of the Mediterranean Sea outflow to the Norwegian\u2010Greenland Sea, Deep Sea Res., Part A, 26, 1199\u20131223, doi:10.1016/0198-0149(79)90064-5.\n* Talley LD, McCartney MS. 1982. Distribution and circulation of Labrador Sea water. Journal of Physical Oceanography, 12(11), 1189-1205.\n* van Aken HM. 2000. The hydrography of the mid-latitude northeast Atlantic Ocean I: the deep water masses. Deep Sea Res. Part I. 47:757\u2013788.\n", "doi": "10.48670/moi-00258", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-wmhe-mow,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterrranean Outflow Water Index from Reanalysis & Multi-Observations Reprocessing"}, "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031": {"abstract": "Arctic Oceans - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00031", "doi": "10.48670/moi-00031", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-arc-phybgcwav-discrete-mynrt-013-031,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean- In Situ Near Real Time Observations"}, "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032": {"abstract": "Baltic Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00032", "doi": "10.48670/moi-00032", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-bal-phybgcwav-discrete-mynrt-013-032,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea- In Situ Near Real Time Observations"}, "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034": {"abstract": "Black Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00033", "doi": "10.48670/moi-00033", "instrument": null, "keywords": "black-sea,coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-blk-phybgcwav-discrete-mynrt-013-034,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea- In-Situ Near Real Time Observations"}, "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050": {"abstract": "Global Ocean- in-situ reprocessed Carbon observations. This product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: Surface Ocean Carbon ATlas SOCATv2024 and GLobal Ocean Data Analysis Project GLODAPv2.2023. \nThe SOCATv2024-OBS dataset contains >38 million observations of fugacity of CO2 of the surface global ocean from 1957 to early 2024. The quality control procedures are described in Bakker et al. (2016). These observations form the basis of the gridded products included in SOCATv2024-GRIDDED: monthly, yearly and decadal averages of fCO2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean.\nGLODAPv2.2023-OBS contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and pH from 1972 to 2021. These data were subjected to an extensive quality control and bias correction described in Olsen et al. (2020). GLODAPv2-GRIDDED contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and pH over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous major iteration of GLODAP, GLODAPv2. \nSOCAT and GLODAP are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles (see References below) in order to support future sustainability of the data products.\n\n**DOI (product):** \nhttps://doi.org/10.17882/99089\n\n**References:**\n\n* Bakker et al., 2016. A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT). Earth Syst. Sci. Data, 8, 383\u2013413, https://doi.org/10.5194/essd-8-383-2016.\n* Lauvset et al. 2024. The annual update GLODAPv2.2023: the global interior ocean biogeochemical data product. Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-468.\n* Lauvset et al., 2016. A new global interior ocean mapped climatology: t\u202f\u00d7\u2009\u202f1\u00b0 GLODAP version 2. Earth Syst. Sci. Data, 8, 325\u2013340, https://doi.org/10.5194/essd-8-325-2016.\n", "doi": "10.17882/99089", "instrument": null, "keywords": "coastal-marine-environment,fugacity-of-carbon-dioxide-in-sea-water,global-ocean,in-situ-observation,insitu-glo-bgc-carbon-discrete-my-013-050,level-3,marine-resources,marine-safety,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,multi-year,oceanographic-geographical-features,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1957-10-22T22:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - In Situ reprocessed carbon observations - SOCATv2024 / GLODAPv2.2023"}, "INSITU_GLO_BGC_DISCRETE_MY_013_046": {"abstract": "For the Global Ocean- In-situ observation delivered in delayed mode. This In Situ delayed mode product integrates the best available version of in situ oxygen, chlorophyll / fluorescence and nutrients data.\n\n**DOI (product):** \nhttps://doi.org/10.17882/86207", "doi": "10.17882/86207", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-bgc-discrete-my-013-046,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-silicate-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,multi-year,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Delayed Mode Biogeochemical product"}, "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030": {"abstract": "Global Ocean - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average. Data are collected mainly through global networks (Argo, OceanSites, GOSUD, EGO) and through the GTS\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00036", "doi": "10.48670/moi-00036", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,global-ocean,in-situ-observation,insitu-glo-phybgcwav-discrete-mynrt-013-030,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- In-Situ Near-Real-Time Observations"}, "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053": {"abstract": "This product integrates sea level observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from the Global telecommunication system (GTS) used by the Met Offices.\n\n**DOI (product):** \nhttps://doi.org/10.17882/93670", "doi": "10.17882/93670", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ssh-discrete-my-013-053,level-2,marine-resources,marine-safety,near-real-time,non-tidal-elevation-of-sea-surface-height,oceanographic-geographical-features,tidal-sea-surface-height-above-reference-datum,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1821-05-25T05:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Delayed Mode Sea level product"}, "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001": {"abstract": "For the Global Ocean- In-situ observation yearly delivery in delayed mode. The In Situ delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for temperature and salinity measurements. These data are collected from main global networks (Argo, GOSUD, OceanSITES, World Ocean Database) completed by European data provided by EUROGOOS regional systems and national system by the regional INS TAC components. It is updated on a yearly basis. This version is a merged product between the previous verion of CORA and EN4 distributed by the Met Office for the period 1950-1990.\n\n**DOI (product):** \nhttps://doi.org/10.17882/46219", "doi": "10.17882/46219", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ts-discrete-my-013-001,level-2,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- CORA- In-situ Observations Yearly Delivery in Delayed Mode"}, "INSITU_GLO_PHY_TS_OA_MY_013_052": {"abstract": "Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the reprocessed in-situ global product CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) using the ISAS software. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n**DOI (product):** \nhttps://doi.org/10.17882/46219", "doi": "10.17882/46219", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ts-oa-my-013-052,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1960-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode"}, "INSITU_GLO_PHY_TS_OA_NRT_013_002": {"abstract": "For the Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the in-situ near real time database are produced monthly. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a support for localized experience (cruises), providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00037", "doi": "10.48670/moi-00037", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ts-oa-nrt-013-002,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- Real time in-situ observations objective analysis"}, "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044": {"abstract": "Global Ocean - This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface and subsurface currents. Current data from 5 different types of instruments are distributed:\n* The drifter's near-surface velocities computed from their position measurements. In addition, a wind slippage correction is provided from 1993. Information on the presence of the drogue of the drifters is also provided.\n* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency (HF) radars that are part of the European HF radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata.\n* The zonal and meridional velocities, at parking depth (mostly around 1000m) and at the surface, calculated along the trajectories of the floats which are part of the Argo Program.\n* The velocity profiles within the water column coming from Acoustic Doppler Current Profiler (vessel mounted ADCP, Moored ADCP, saildrones) platforms\n* The near-surface and subsurface velocities calculated from gliders (autonomous underwater vehicle) trajectories\n\n**DOI (product):**\nhttps://doi.org/10.17882/86236", "doi": "10.17882/86236", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,insitu-glo-phy-uv-discrete-my-013-044,level-2,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,oceanographic-geographical-features,sea-water-temperature,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1979-12-11T04:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048": {"abstract": "This product is entirely dedicated to ocean current data observed in near-real time. Current data from 3 different types of instruments are distributed:\n* The near-surface zonal and meridional velocities calculated along the trajectories of the drifting buoys which are part of the DBCP\u2019s Global Drifter Program. These data are delivered together with wind stress components, surface temperature and a wind-slippage correction for drogue-off and drogue-on drifters trajectories. \n* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency radars that are part of the European High Frequency radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata.\n* The zonal and meridional velocities, at parking depth and in surface, calculated along the trajectories of the floats which are part of the Argo Program.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00041", "doi": "10.48670/moi-00041", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,insitu-glo-phy-uv-discrete-nrt-013-048,level-2,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,oceanographic-geographical-features,sea-water-temperature,surface-eastward-sea-water-velocity,surface-northward-sea-water-velocity,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1986-06-02T09:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- in-situ Near real time observations of ocean currents"}, "INSITU_GLO_WAV_DISCRETE_MY_013_045": {"abstract": "These products integrate wave observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from National Data Centers (NODCs) and JCOMM global systems (OceanSITES, DBCP) and the Global telecommunication system (GTS) used by the Met Offices.\n\n**DOI (product):** \nhttps://doi.org/10.17882/70345", "doi": "10.17882/70345", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-wav-discrete-my-013-045,level-2,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-surface-wave-mean-period,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-04-27T18:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Delayed Mode Wave product"}, "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033": {"abstract": "IBI Seas - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00043", "doi": "10.48670/moi-00043", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,iberian-biscay-irish-seas,in-situ-observation,insitu-ibi-phybgcwav-discrete-mynrt-013-033,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Iberian Biscay Irish Ocean- In-Situ Near Real Time Observations"}, "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035": {"abstract": "Mediterranean Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00044", "doi": "10.48670/moi-00044", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-med-phybgcwav-discrete-mynrt-013-035,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea- In-Situ Near Real Time Observations"}, "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036": {"abstract": "NorthWest Shelf area - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00045", "doi": "10.48670/moi-00045", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-nws-phybgcwav-discrete-mynrt-013-036,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Ocean In-Situ Near Real Time observations"}, "MEDSEA_ANALYSISFORECAST_BGC_006_014": {"abstract": "The biogeochemical analysis and forecasts for the Mediterranean Sea at 1/24\u00b0 of horizontal resolution (ca. 4 km) are produced by means of the MedBFM4 model system. MedBFM4, which is run by OGS (IT), consists of the coupling of the multi-stream atmosphere radiative model OASIM, the multi-stream in-water radiative and tracer transport model OGSTM_BIOPTIMOD v4.6, and the biogeochemical flux model BFM v5.3. Additionally, MedBFM4 features the 3D variational data assimilation scheme 3DVAR-BIO v4.1 with the assimilation of surface chlorophyll (CMEMS-OCTAC NRT product) and of vertical profiles of chlorophyll, nitrate and oxygen (BGC-Argo floats provided by CORIOLIS DAC). The biogeochemical MedBFM system, which is forced by the NEMO-OceanVar model (MEDSEA_ANALYSIS_FORECAST_PHY_006_013), produces one day of hindcast and ten days of forecast (every day) and seven days of analysis (weekly on Tuesday).\nSalon, S.; Cossarini, G.; Bolzon, G.; Feudale, L.; Lazzari, P.; Teruzzi, A.; Solidoro, C., and Crise, A. (2019) Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. Ocean Science, 15, pp.997\u20131022. DOI: https://doi.org/10.5194/os-15-997-2019\n\n_DOI (Product)_: \nhttps://doi.org/10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbfm4\n\n**References:**\n\n* Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Teruzzi, A., Di Biagio, V., Coidessa, G., Alvarez, E., Amadio, C., & Cossarini, G. (2022). Mediterranean Sea Biogeochemical Analysis and Forecast (CMEMS MED-Biogeochemistry, MedBFM4 system) (Version 1) [Data set]. Copernicus Marine Service. https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_BGC_006_014_MEDBFM4\n", "doi": "10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbfm4", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,medsea-analysisforecast-bgc-006-014,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "MEDSEA_ANALYSISFORECAST_PHY_006_013": {"abstract": "The physical component of the Mediterranean Forecasting System (Med-Physics) is a coupled hydrodynamic-wave model implemented over the whole Mediterranean Basin including tides. The model horizontal grid resolution is 1/24\u02da (ca. 4 km) and has 141 unevenly spaced vertical levels.\nThe hydrodynamics are supplied by the Nucleous for European Modelling of the Ocean NEMO (v4.2) and include the representation of tides, while the wave component is provided by Wave Watch-III (v6.07) coupled through OASIS; the model solutions are corrected by a 3DVAR assimilation scheme (OceanVar) for temperature and salinity vertical profiles and along track satellite Sea Level Anomaly observations. \n\n_Product Citation_: Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\"\n\n_DOI (Product)_:\nhttps://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8\n\n**References:**\n\n* Clementi, E., Aydogdu, A., Goglio, A. C., Pistoia, J., Escudier, R., Drudi, M., Grandi, A., Mariani, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Coppini, G., Masina, S., & Pinardi, N. (2021). Mediterranean Sea Physical Analysis and Forecast (CMEMS MED-Currents, EAS6 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8\n", "doi": "10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-analysisforecast-phy-006-013,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Physics Analysis and Forecast"}, "MEDSEA_ANALYSISFORECAST_WAV_006_017": {"abstract": "MEDSEA_ANALYSISFORECAST_WAV_006_017 is the nominal wave product of the Mediterranean Sea Forecasting system, composed by hourly wave parameters at 1/24\u00ba horizontal resolution covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The waves forecast component (Med-WAV system) is a wave model based on the WAM Cycle 6. The Med-WAV modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins and the model solutions are corrected by an optimal interpolation data assimilation scheme of all available along track satellite significant wave height observations. The atmospheric forcing is provided by the operational ECMWF Numerical Weather Prediction model and the wave model is forced with hourly averaged surface currents and sea level obtained from MEDSEA_ANALYSISFORECAST_PHY_006_013 at 1/24\u00b0 resolution. The model uses wave spectra for Open Boundary Conditions from GLOBAL_ANALYSIS_FORECAST_WAV_001_027 product. The wave system includes 2 forecast cycles providing twice per day a Mediterranean wave analysis and 10 days of wave forecasts.\n\n_Product Citation_: Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n**DOI (product)**: https://doi.org/10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4\n\n**References:**\n\n* Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2023). Mediterranean Sea Waves Analysis and Forecast (Copernicus Marine Service MED-Waves, MEDWA\u039c4 system) (Version 1) [Data set]. Copernicus Marine Service (CMS). https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4\n", "doi": "10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4", "instrument": null, "keywords": "coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-analysisforecast-wav-006-017,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-04-19T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Waves Analysis and Forecast"}, "MEDSEA_MULTIYEAR_BGC_006_008": {"abstract": "The Mediterranean Sea biogeochemical reanalysis at 1/24\u00b0 of horizontal resolution (ca. 4 km) covers the period from Jan 1999 to 1 month to the present and is produced by means of the MedBFM3 model system. MedBFM3, which is run by OGS (IT), includes the transport model OGSTM v4.0 coupled with the biogeochemical flux model BFM v5 and the variational data assimilation module 3DVAR-BIO v2.1 for surface chlorophyll. MedBFM3 is forced by the physical reanalysis (MEDSEA_MULTIYEAR_PHY_006_004 product run by CMCC) that provides daily forcing fields (i.e., currents, temperature, salinity, diffusivities, wind and solar radiation). The ESA-CCI database of surface chlorophyll concentration (CMEMS-OCTAC REP product) is assimilated with a weekly frequency. \n\nCossarini, G., Feudale, L., Teruzzi, A., Bolzon, G., Coidessa, G., Solidoro C., Amadio, C., Lazzari, P., Brosich, A., Di Biagio, V., and Salon, S., 2021. High-resolution reanalysis of the Mediterranean Sea biogeochemistry (1999-2019). Frontiers in Marine Science. Front. Mar. Sci. 8:741486.doi: 10.3389/fmars.2021.741486\n\n_Product Citation_: Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n_DOI (Product)_: https://doi.org/10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3\n\n_DOI (Interim dataset)_:\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I\n\n**References:**\n\n* Teruzzi, A., Di Biagio, V., Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Coidessa, G., & Cossarini, G. (2021). Mediterranean Sea Biogeochemical Reanalysis (CMEMS MED-Biogeochemistry, MedBFM3 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3\n* Teruzzi, A., Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Di Biagio, V., Coidessa, G., & Cossarini, G. (2021). Mediterranean Sea Biogeochemical Reanalysis INTERIM (CMEMS MED-Biogeochemistry, MedBFM3i system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS) https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I\n", "doi": "10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,medsea-multiyear-bgc-006-008,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "MEDSEA_MULTIYEAR_PHY_006_004": {"abstract": "The Med MFC physical multiyear product is generated by a numerical system composed of an hydrodynamic model, supplied by the Nucleous for European Modelling of the Ocean (NEMO) and a variational data assimilation scheme (OceanVAR) for temperature and salinity vertical profiles and satellite Sea Level Anomaly along track data. It contains a reanalysis dataset and an interim dataset which covers the period after the reanalysis until 1 month before present. The model horizontal grid resolution is 1/24\u02da (ca. 4-5 km) and the unevenly spaced vertical levels are 141. \n\n**Product Citation**: \nPlease refer to our Technical FAQ for citing products\n\n**DOI (Product)**: \nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n\n**DOI (Interim dataset)**:\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I\n\n**References:**\n\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., Omar, M., Masina, S., Coppini G., Pinardi, N. (2021). A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, 9, 1060, https://www.frontiersin.org/article/10.3389/feart.2021.702285, DOI=10.3389/feart.2021.702285\n* Nigam, T., Escudier, R., Pistoia, J., Aydogdu, A., Omar, M., Clementi, E., Cipollone, A., Drudi, M., Grandi, A., Mariani, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2021). Mediterranean Sea Physical Reanalysis INTERIM (CMEMS MED-Currents, E3R1i system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I\n", "doi": "10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-multiyear-phy-006-004,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1987-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Physics Reanalysis"}, "MEDSEA_MULTIYEAR_WAV_006_012": {"abstract": "MEDSEA_MULTIYEAR_WAV_006_012 is the multi-year wave product of the Mediterranean Sea Waves forecasting system (Med-WAV). It contains a Reanalysis dataset, an Interim dataset covering the period after the reanalysis until 1 month before present and a monthly climatological dataset (reference period 1993-2016). The Reanalysis dataset is a multi-year wave reanalysis starting from January 1985, composed by hourly wave parameters at 1/24\u00b0 horizontal resolution, covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The Med-WAV modelling system is based on wave model WAM 4.6.2 and has been developed as a nested sequence of two computational grids (coarse and fine) to ensure that swell propagating from the North Atlantic (NA) towards the strait of Gibraltar is correctly entering the Mediterranean Sea. The coarse grid covers the North Atlantic Ocean from 75\u00b0W to 10\u00b0E and from 70\u00b0 N to 10\u00b0 S in 1/6\u00b0 resolution while the nested fine grid covers the Mediterranean Sea from 18.125\u00b0 W to 36.2917\u00b0 E and from 30.1875\u00b0 N to 45.9792\u00b0 N with a 1/24\u00b0 resolution. The modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins. The wave system also includes an optimal interpolation assimilation scheme assimilating significant wave height along track satellite observations available through CMEMS and it is forced with daily averaged currents from Med-Physics and with 1-h, 0.25\u00b0 horizontal-resolution ERA5 reanalysis 10m-above-sea-surface winds from ECMWF. \n\n_Product Citation_: Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n_DOI (Product)_: https://doi.org/10.25423/cmcc/medsea_multiyear_wav_006_012 \n\n_DOI (Interim dataset)_: \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I \n \n_DOI (climatological dataset)_: \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_CLIM \n\n**DOI (Interim dataset)**:\nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I\n\n**References:**\n\n* Korres, G., Ravdas, M., Denaxa, D., & Sotiropoulou, M. (2021). Mediterranean Sea Waves Reanalysis INTERIM (CMEMS Med-Waves, MedWAM3I system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I\n* Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2023). Mediterranean Sea Waves Monthly Climatology (CMS Med-Waves, MedWAM3 system) (Version 1) [Data set]. Copernicus Marine Service (CMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012_CLIM\n* Korres, G., Ravdas, M., Zacharioudaki, A., Denaxa, D., & Sotiropoulou, M. (2021). Mediterranean Sea Waves Reanalysis (CMEMS Med-Waves, MedWAM3 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012\n", "doi": "10.25423/cmcc/medsea_multiyear_wav_006_012", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-multiyear-wav-006-012,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Waves Reanalysis"}, "MEDSEA_OMI_OHC_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 700 m depth:\nOHC=\u222b_(z_1)^(z_2)\u03c1_0 c_p (T_yr-T_clim )dz \t\t\t\t\t\t\t\t[1]\nwith a reference density of = 1030 kgm-3 and a specific heat capacity of cp = 3980 J kg-1 \u00b0C-1 (e.g. von Schuckmann et al., 2009).\nTime series of annual mean values area averaged ocean heat content is provided for the Mediterranean Sea (30\u00b0N, 46\u00b0N; 6\u00b0W, 36\u00b0E) and is evaluated for topography deeper than 300m.\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future.\nThe quality evaluation of MEDSEA_OMI_OHC_area_averaged_anomalies is based on the \u201cmulti-product\u201d approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean\u2019s experience (Masina et al., 2017). \nSix global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:\n\tThe Mediterranean Sea Reanalysis at 1/24 degree horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)\n\tFour global reanalyses at 1/4 degree horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031): \nGLORYS, C-GLORS, ORAS5, FOAM\n\tTwo observation based products: \nCORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) and \nARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). \nDetails on the products are delivered in the PUM and QUID of this OMI. \n\n**CMEMS KEY FINDINGS**\n\nThe ensemble mean ocean heat content anomaly time series over the Mediterranean Sea shows a continuous increase in the period 1993-2019 at rate of 1.4\u00b10.3 W/m2 in the upper 700m. After 2005 the rate has clearly increased with respect the previous decade, in agreement with Iona et al. (2018).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00261\n\n**References:**\n\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Iona, A., A. Theodorou, S. Sofianos, S. Watelet, C. Troupin, J.-M. Beckers, 2018: Mediterranean Sea climatic indices: monitoring long term variability and climate changes, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2018-51, in review.\n* Masina S., A. Storto, N. Ferry, M. Valdivieso, K. Haines, M. Balmaseda, H. Zuo, M. Drevillon, L. Parent, 2017: An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Climate Dynamics, 49 (3): 813-841. DOI: 10.1007/s00382-015-2728-5\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann et al., 2018: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, s1-s142, DOI: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00261", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,medsea-omi-ohc-area-averaged-anomalies,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Ocean Heat Content Anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS MEDSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_WAV_006_012) and the Analysis product (MEDSEA_ANALYSIS_FORECAST_WAV_006_017).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2014). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions.\nThe Mediterranean Sea is an almost enclosed basin where the complexity of its orographic characteristics deeply influences the atmospheric circulation at local scale, giving rise to strong regional wind regimes (Drobinski et al. 2018). Therefore, since waves are primarily driven by winds, high waves are present over most of the Mediterranean Sea and tend to reach the highest values where strong wind and long fetch (i.e. the horizontal distance over which wave-generating winds blow) are simultaneously present (Lionello et al. 2006). Specifically, as seen in figure and in agreement with other studies (e.g. Sartini et al. 2017), the highest values (5 \u2013 6 m in figure, top) extend from the Gulf of Lion to the southwestern Sardinia through the Balearic Sea and are sustained southwards approaching the Algerian coast. They result from northerly winds dominant in the western Mediterranean Sea (Mistral or Tramontana), that become stronger due to orographic effects (Menendez et al. 2014), and act over a large area. In the Ionian Sea, the northerly Mistral wind is still the main cause of high waves (4-5 m in figure, top). In the Aegean and Levantine Seas, high waves (4-5 m in figure, top) are caused by the northerly Bora winds, prevalent in winter, and the northerly Etesian winds, prevalent in summer (Lionello et al. 2006; Chronis et al. 2011; Menendez et al. 2014). In general, northerly winds are responsible for most high waves in the Mediterranean (e.g. Chronis et al. 2011; Menendez et al. 2014). In agreement with figure (top), studies on the eastern Mediterranean and the Hellenic Seas have found that the typical wave height range in the Aegean Sea is similar to the one observed in the Ionian Sea despite the shorter fetches characterizing the former basin (Zacharioudaki et al. 2015). This is because of the numerous islands in the Aegean Sea which cause wind funneling and enhance the occurrence of extreme winds and thus of extreme waves (Kotroni et al. 2001). Special mention should be made of the high waves, sustained throughout the year, observed east and west of the island of Crete, i.e. around the exiting points of the northerly airflow in the Aegean Sea (Zacharioudaki et al. 2015). This airflow is characterized by consistently high magnitudes that are sustained during all seasons in contrast to other airflows in the Mediterranean Sea that exhibit a more pronounced seasonality (Chronis et al. 2011). \n\n**CMEMS KEY FINDINGS**\n\nIn 2020 (bottom panel), higher-than-average values of the 99th percentile of Significant Wave Height are seen over most of the northern Mediterranean Sea, in the eastern Alboran Sea, and along stretches of the African coast (Tunisia, Libya and Egypt). In many cases they exceed the climatic standard deviation. Regions where the climatic standard deviation is exceeded twice are the European and African coast of the eastern Alboran Sea, a considerable part of the eastern Spanish coast, the Ligurian Sea and part of the east coast of France as well as areas of the southern Adriatic. These anomalies correspond to the maximum positive anomalies computed in the Mediterranean Sea for year 2020 with values that reach up to 1.1 m. Spatially constrained maxima are also found at other coastal stretches (e.g. Algeri, southeast Sardinia). Part of the positive anomalies found along the French and Spanish coast, including the coast of the Balearic Islands, can be associated with the wind storm \u201cGloria\u201d (19/1 \u2013 24/1) during which exceptional eastern winds originated in the Ligurian Sea and propagated westwards. The storm, which was of a particularly high intensity and long duration, caused record breaking wave heights in the region, and, in return, great damage to the coast (Amores et al., 2020; de Alfonso et al., 2021). Other storms that could have contributed to the positive anomalies observed in the western Mediterranean Sea include: storm Karine (25/2 \u2013 5/4), which caused high waves from the eastern coast of Spain to the Balearic Islands (Copernicus, Climate Change Service, 2020); storm Bernardo (7/11 \u2013 18/11) which also affected the Balearic islands and the Algerian coast and; storm Herv\u00e9 (2/2 \u2013 8/2) during which the highest wind gust was recorded at north Corsica (Wikiwand, 2021). In the eastern Mediterranean Sea, the medicane Ianos (14/9 \u2013 21/9) may have contributed to the positive anomalies shown in the central Ionian Sea since this area coincides with the area of peak wave height values during the medicane (Copernicus, 2020a and Copernicus, 2020b). Otherwise, higher-than-average values in the figure are the result of severe, yet not unusual, wind events, which occurred during the year. Negative anomalies occur over most of the southern Mediterranean Sea, east of the Alboran Sea. The maximum negative anomalies reach about -1 m and are located in the southeastern Ionian Sea and west of the south part of mainland Greece as well as in coastal locations of the north and east Aegean They appear to be quite unusual since they are greater than two times the climatic standard deviation in the region. They could imply less severe southerly wind activity during 2020 (Drobinski et al., 2018). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00262\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Amores, A., Marcos, M., Carri\u00f3, Di.S., Gomez-Pujol, L., 2020. Coastal impacts of Storm Gloria (January 2020) over the north-western Mediterranean. Nat. Hazards Earth Syst. Sci. 20, 1955\u20131968. doi:10.5194/nhess-20-1955-2020\n* Chronis T, Papadopoulos V, Nikolopoulos EI. 2011. QuickSCAT observations of extreme wind events over the Mediterranean and Black Seas during 2000-2008. Int J Climatol. 31: 2068\u20132077.\n* Copernicus: Climate Change Service. 2020a (Last accessed July 2021): URL: https://surfobs.climate.copernicus.eu/stateoftheclimate/march2020.php\n* Copernicus, Copernicus Marine Service. 2020b (Last accessed July 2021): URL: https://marine.copernicus.eu/news/following-cyclone-ianos-across-mediterranean-sea\n* de Alfonso, M., Lin-Ye, J., Garc\u00eda-Valdecasas, J.M., P\u00e9rez-Rubio, S., Luna, M.Y., Santos-Mu\u00f1oz, D., Ruiz, M.I., P\u00e9rez-G\u00f3mez, B., \u00c1lvarez-Fanjul, E., 2021. Storm Gloria: Sea State Evolution Based on in situ Measurements and Modeled Data and Its Impact on Extreme Values. Front. Mar. Sci. 8, 1\u201317. doi:10.3389/fmars.2021.646873\n* Drobinski P, Alpert P, Cavicchia L, Flaoumas E, Hochman A, Kotroni V. 2018. Strong winds Observed trends, future projections, Sub-chapter 1.3.2, p. 115-122. In: Moatti JP, Thi\u00e9bault S (dir.). The Mediterranean region under climate change: A scientific update. Marseille: IRD \u00c9ditions.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2014. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2014 In book: Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* Kotroni V, Lagouvardos K, Lalas D. 2001. The effect of the island of Crete on the Etesian winds over the Aegean Sea. Q J R Meteorol Soc. 127: 1917\u20131937. doi:10.1002/qj.49712757604\n* Lionello P, Rizzoli PM, Boscolo R. 2006. Mediterranean climate variability, developments in earth and environmental sciences. Elsevier.\n* Menendez M, Garc\u00eda-D\u00edez M, Fita L, Fern\u00e1ndez J, M\u00e9ndez FJ, Guti\u00e9rrez JM. 2014. High-resolution sea wind hindcasts over the Mediterranean area. Clim Dyn. 42:1857\u20131872.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sartini L, Besio G, Cassola F. 2017. Spatio-temporal modelling of extreme wave heights in the Mediterranean Sea. Ocean Modelling, 117, 52-69.\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Wikiwand: 2019 - 20 European windstorm season. URL: https://www.wikiwand.com/en/2019%E2%80%9320_European_windstorm_season\n* Zacharioudaki A, Korres G, Perivoliotis L, 2015. Wave climate of the Hellenic Seas obtained from a wave hindcast for the period 1960\u20132001. Ocean Dynamics. 65: 795\u2013816. https://doi.org/10.1007/s10236-015-0840-z\n", "doi": "10.48670/moi-00262", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-seastate-extreme-var-swh-mean-and-anomaly,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Significant Wave Height extreme from Reanalysis"}, "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS MEDSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_PHY_006_004) and the Analysis product (MEDSEA_ANALYSISFORECAST_PHY_006_013).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1987-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Near Real Time product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. In recent decades (from mid \u201880s) the Mediterranean Sea showed a trend of increasing temperatures (Ducrocq et al., 2016), which has been observed also by means of the CMEMS SST_MED_SST_L4_REP_OBSERVATIONS_010_021 satellite product and reported in the following CMEMS OMI: MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies and MEDSEA_OMI_TEMPSAL_sst_trend.\nThe Mediterranean Sea is a semi-enclosed sea characterized by an annual average surface temperature which varies horizontally from ~14\u00b0C in the Northwestern part of the basin to ~23\u00b0C in the Southeastern areas. Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions. The Mediterranean Sea annual 99th percentile presents a significant interannual and multidecadal variability with a significant increase starting from the 80\u2019s as shown in Marb\u00e0 et al. (2015) which is also in good agreement with the multidecadal change of the mean SST reported in Mariotti et al. (2012). Moreover the spatial variability of the SST 99th percentile shows large differences at regional scale (Darmariaki et al., 2019; Pastor et al. 2018).\n\n**CMEMS KEY FINDINGS**\n\nThe Mediterranean mean Sea Surface Temperature 99th percentile evaluated in the period 1987-2019 (upper panel) presents highest values (~ 28-30 \u00b0C) in the eastern Mediterranean-Levantine basin and along the Tunisian coasts especially in the area of the Gulf of Gabes, while the lowest (~ 23\u201325 \u00b0C) are found in the Gulf of Lyon (a deep water formation area), in the Alboran Sea (affected by incoming Atlantic waters) and the eastern part of the Aegean Sea (an upwelling region). These results are in agreement with previous findings in Darmariaki et al. (2019) and Pastor et al. (2018) and are consistent with the ones presented in CMEMS OSR3 (Alvarez Fanjul et al., 2019) for the period 1993-2016.\nThe 2020 Sea Surface Temperature 99th percentile anomaly map (bottom panel) shows a general positive pattern up to +3\u00b0C in the North-West Mediterranean area while colder anomalies are visible in the Gulf of Lion and North Aegean Sea . This Ocean Monitoring Indicator confirms the continuous warming of the SST and in particular it shows that the year 2020 is characterized by an overall increase of the extreme Sea Surface Temperature values in almost the whole domain with respect to the reference period. This finding can be probably affected by the different dataset used to evaluate this anomaly map: the 2020 Sea Surface Temperature 99th percentile derived from the Near Real Time Analysis product compared to the mean (1987-2019) Sea Surface Temperature 99th percentile evaluated from the Reanalysis product which, among the others, is characterized by different atmospheric forcing).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00266\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Darmaraki S, Somot S, Sevault F, Nabat P, Cabos W, Cavicchia L, et al. 2019. Future evolution of marine heatwaves in the Mediterranean Sea. Clim. Dyn. 53, 1371\u20131392. doi: 10.1007/s00382-019-04661-z\n* Ducrocq V., Drobinski P., Gualdi S., Raimbault P. 2016. The water cycle in the Mediterranean. Chapter 1.2.1 in The Mediterranean region under climate change. IRD E\u0301ditions. DOI : 10.4000/books.irdeditions.22908.\n* Marb\u00e0 N, Jord\u00e0 G, Agust\u00ed S, Girard C, Duarte CM. 2015. Footprints of climate change on Mediterranean Sea biota. Front.Mar.Sci.2:56. doi: 10.3389/fmars.2015.00056\n* Mariotti A and Dell\u2019Aquila A. 2012. Decadal climate variability in the Mediterranean region: roles of large-scale forcings and regional processes. Clim Dyn. 38,1129\u20131145. doi:10.1007/s00382-011-1056-7\n* Pastor F, Valiente JA, Palau JL. 2018. Sea Surface Temperature in the Mediterranean: Trends and Spatial Patterns (1982\u20132016). Pure Appl. Geophys, 175: 4017. https://doi.org/10.1007/s00024-017-1739-zP\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Buongiorno Nardelli B. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sens. 2020, 12, 132.\n", "doi": "10.48670/moi-00266", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-tempsal-extreme-var-temp-mean-and-anomaly,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Surface Temperature extreme from Reanalysis"}, "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe medsea_omi_tempsal_sst_area_averaged_anomalies product for 2023 includes unfiltered Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the Mediterranean Sea, and 24-month filtered SST anomalies, obtained by using the X11-seasonal adjustment procedure (see e.g. Pezzulli et al., 2005; Pisano et al., 2020). This OMI is derived from the CMEMS Reprocessed Mediterranean L4 SST satellite product (SST_MED_SST_L4_REP_OBSERVATIONS_010_021, see also the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf), which provides the SSTs used to compute the evolution of SST anomalies (unfiltered and filtered) over the Mediterranean Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Mediterranean Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Anomalies are computed against the 1991-2020 reference period. The 30-year climatology 1991-2020 is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). The Mediterranean Sea is a climate change hotspot (Giorgi F., 2006). Indeed, Mediterranean SST has experienced a continuous warming trend since the beginning of 1980s (e.g., Pisano et al., 2020; Pastor et al., 2020). Specifically, since the beginning of the 21st century (from 2000 onward), the Mediterranean Sea featured the highest SSTs and this warming trend is expected to continue throughout the 21st century (Kirtman et al., 2013). \n\n**KEY FINDINGS**\n\nDuring 2023, the Mediterranean Sea continued experiencing the intense sea surface temperatures\u2019 warming (marine heatwave event) that started in May 2022 (Marullo et al., 2023). The basin average SST anomaly was 0.9 \u00b1 0.1 \u00b0C in 2023, the highest in this record. The Mediterranean SST warming started in May 2022, when the mean anomaly increased abruptly from 0.01 \u00b0C (April) to 0.76 \u00b0C (May), reaching the highest values during June (1.66 \u00b0C) and July (1.52 \u00b0C), and persisting until summer 2023 with anomalies around 1 \u00b0C above the 1991-2020 climatology. The peak of July 2023 (1.76 \u00b0C) set the record of highest SST anomaly ever recorded since 1982. The 2022/2023 Mediterranean marine heatwave is comparable to that occurred in 2003 (see e.g. Olita et al., 2007) in terms of anomaly magnitude but longer in duration.\nOver the period 1982-2023, the Mediterranean SST has warmed at a rate of 0.041 \u00b1 0.001 \u00b0C/year, which corresponds to an average increase of about 1.7 \u00b0C during these last 42 years. Within its error (namely, the 95% confidence interval), this warming trend is consistent with recent trend estimates in the Mediterranean Sea (Pisano et al., 2020; Pastor et al., 2020). However, though the linear trend being constantly increasing during the whole period, the picture of the Mediterranean SST trend in 2022 seems to reveal a restarting after the pause occurred in the last years (since 2015-2021).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00268\n\n**References:**\n\n* Giorgi, F., 2006. Climate change hot-spots. Geophys. Res. Lett., 33:L08707, https://doi.org/10.1029/2006GL025734\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. doi:10.1080/1755876X.2016.1273446.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Pisano, A., Marullo, S., Artale, V., Falcini, F., Yang, C., Leonelli, F. E., Santoleri, R. and Buongiorno Nardelli, B.: New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations, Remote Sens., 12(1), 132, doi:10.3390/rs12010132, 2020.\n* Pastor, F., Valiente, J. A., & Khodayar, S. (2020). A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature. Remote Sensing, 12(17), 2687.\n* Olita, A., Sorgente, R., Natale, S., Gaber\u0161ek, S., Ribotti, A., Bonanno, A., & Patti, B. (2007). Effects of the 2003 European heatwave on the Central Mediterranean Sea: surface fluxes and the dynamical response. Ocean Science, 3(2), 273-289.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00268", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-tempsal-sst-area-averaged-anomalies,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Surface Temperature time series and trend from Observations Reprocessing"}, "MEDSEA_OMI_TEMPSAL_sst_trend": {"abstract": "**DEFINITION**\n\nThe medsea_omi_tempsal_sst_trend product includes the cumulative/net Sea Surface Temperature (SST) trend for the Mediterranean Sea over the period 1982-2023, i.e. the rate of change (\u00b0C/year) multiplied by the number years in the time series (42 years). This OMI is derived from the CMEMS Reprocessed Mediterranean L4 SST product (SST_MED_SST_L4_REP_OBSERVATIONS_010_021, see also the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf), which provides the SSTs used to compute the SST trend over the Mediterranean Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Mediterranean Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005; Pisano et al., 2020), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterize the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). The Mediterranean Sea is a climate change hotspot (Giorgi F., 2006). Indeed, Mediterranean SST has experienced a continuous warming trend since the beginning of 1980s (e.g., Pisano et al., 2020; Pastor et al., 2020). Specifically, since the beginning of the 21st century (from 2000 onward), the Mediterranean Sea featured the highest SSTs and this warming trend is expected to continue throughout the 21st century (Kirtman et al., 2013). \n\n**KEY FINDINGS**\n\nOver the past four decades (1982-2023), the Mediterranean Sea surface temperature (SST) warmed at a rate of 0.041 \u00b1 0.001 \u00b0C per year, corresponding to a mean surface temperature warming of about 1.7 \u00b0C. The spatial pattern of the Mediterranean SST trend shows a general warming tendency, ranging from 0.002 \u00b0C/year to 0.063 \u00b0C/year. Overall, a higher SST trend intensity characterizes the Eastern and Central Mediterranean basin with respect to the Western basin. In particular, the Balearic Sea, Tyrrhenian and Adriatic Seas, as well as the northern Ionian and Aegean-Levantine Seas show the highest SST trends (from 0.04 \u00b0C/year to 0.05 \u00b0C/year on average). Trend patterns of warmer intensity characterize some of main sub-basin Mediterranean features, such as the Pelops Anticyclone, the Cretan gyre and the Rhodes Gyre. On the contrary, less intense values characterize the southern Mediterranean Sea (toward the African coast), where the trend attains around 0.025 \u00b0C/year. The SST warming rate spatial change, mostly showing an eastward increase pattern (see, e.g., Pisano et al., 2020, and references therein), i.e. the Levantine basin getting warm faster than the Western, appears now to have tilted more along a North-South direction.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00269\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Giorgi, F., 2006. Climate change hot-spots. Geophys. Res. Lett., 33:L08707, https://doi.org/10.1029/2006GL025734 Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Kirtman, B., Power, S. B, Adedoyin, J. A., Boer, G. J., Bojariu, R. et al., 2013. Near-term climate change: Projections and Predictability. In: Stocker, T.F., et al. (Eds.), Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pastor, F., Valiente, J. A., & Khodayar, S. (2020). A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature. Remote Sensing, 12(17), 2687.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Pisano, A., Marullo, S., Artale, V., Falcini, F., Yang, C., Leonelli, F. E., Santoleri, R. and Buongiorno Nardelli, B.: New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations, Remote Sens., 12(1), 132, doi:10.3390/rs12010132, 2020.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. doi:10.1080/1755876X.2016.1273446.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00269", "instrument": null, "keywords": "change-over-time-in-sea-surface-temperature,coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-tempsal-sst-trend,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009": {"abstract": "This product consists of vertical profiles of the concentration of nutrients (nitrates, phosphates, and silicates) and carbonate system variables (total alkalinity, dissolved inorganic carbon, pH, and partial pressure of carbon dioxide), computed for each Argo float equipped with an oxygen sensor.\nThe method called CANYON (Carbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) is based on a neural network trained using high-quality nutrient data collected over the last 30 years (GLODAPv2 database, https://www.glodap.info/). The method is applied to each Argo float equipped with an oxygen sensor using as input the properties measured by the float (pressure, temperature, salinity, oxygen), and its date and position.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00048\n\n**References:**\n\n* Sauzede R., H. C. Bittig, H. Claustre, O. Pasqueron de Fommervault, J.-P. Gattuso, L. Legendre and K. S. Johnson, 2017: Estimates of Water-Column Nutrient Concentrations and Carbonate System Parameters in the Global Ocean: A novel Approach Based on Neural Networks. Front. Mar. Sci. 4:128. doi: 10.3389/fmars.2017.00128.\n* Bittig H. C., T. Steinhoff, H. Claustre, B. Fiedler, N. L. Williams, R. Sauz\u00e8de, A. K\u00f6rtzinger and J.-P. Gattuso,2018: An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks. Front. Mar. Sci. 5:328. doi: 10.3389/fmars.2018.00328.\n", "doi": "10.48670/moi-00048", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,moles-of-nitrate-per-unit-mass-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,moles-of-phosphate-per-unit-mass-in-sea-water,moles-of-silicate-per-unit-mass-in-sea-water,multi-year,multiobs-glo-bgc-nutrients-carbon-profiles-mynrt-015-009,none,oceanographic-geographical-features,partial-pressure-of-carbon-dioxide-in-sea-water,sea-water-ph-reported-on-total-scale,sea-water-pressure,sea-water-salinity,sea-water-temperature,total-alkalinity-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nutrient and carbon profiles vertical distribution"}, "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010": {"abstract": "This product consists of 3D fields of Particulate Organic Carbon (POC), Particulate Backscattering coefficient (bbp) and Chlorophyll-a concentration (Chla) at depth. The reprocessed product is provided at 0.25\u00b0x0.25\u00b0 horizontal resolution, over 36 levels from the surface to 1000 m depth. \nA neural network method estimates both the vertical distribution of Chla concentration and of particulate backscattering coefficient (bbp), a bio-optical proxy for POC, from merged surface ocean color satellite measurements with hydrological properties and additional relevant drivers. \n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00046\n\n**References:**\n\n* Sauzede R., H. Claustre, J. Uitz, C. Jamet, G. Dall\u2019Olmo, F. D\u2019Ortenzio, B. Gentili, A. Poteau, and C. Schmechtig, 2016: A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient, J. Geophys. Res. Oceans, 121, doi:10.1002/2015JC011408.\n", "doi": "10.48670/moi-00046", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,multi-year,multiobs-glo-bio-bgc-3d-rep-015-010,none,oceanographic-geographical-features,satellite-observation,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1998-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008": {"abstract": "This product corresponds to a REP L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 0.25\u00b0 x 0.25\u00b0 regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al. 2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00047\n\n**References:**\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Chau, T.-T.-T., Chevallier, F., & Gehlen, M. (2024). Global analysis of surface ocean CO2 fugacity and air-sea fluxes with low latency. Geophysical Research Letters, 51, e2023GL106670. https://doi.org/10.1029/2023GL106670\n* Chau, T.-T.-T., Gehlen, M., Metzl, N., and Chevallier, F.: CMEMS-LSCE: a global, 0.25\u00b0, monthly reconstruction of the surface ocean carbonate system, Earth Syst. Sci. Data, 16, 121\u2013160, https://doi.org/10.5194/essd-16-121-2024, 2024.\n", "doi": "10.48670/moi-00047", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-bio-carbon-surface-mynrt-015-008,none,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,total-alkalinity-in-sea-water,uncertainty-surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,uncertainty-surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Surface ocean carbon fields"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008": {"abstract": "This product corresponds to a REP L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 0.25\u00b0 x 0.25\u00b0 regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al. 2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\n**Product Citation**: Please refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00047\n\n**References:**\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n", "doi": "10.48670/moi-00047", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-bio-carbon-surface-rep-015-008,none,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,total-alkalinity-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "LSCE (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Surface Carbon"}, "MULTIOBS_GLO_PHY_MYNRT_015_003": {"abstract": "This product is a L4 REP and NRT global total velocity field at 0m and 15m together wiht its individual components (geostrophy and Ekman) and related uncertainties. It consists of the zonal and meridional velocity at a 1h frequency and at 1/4 degree regular grid. The total velocity fields are obtained by combining CMEMS satellite Geostrophic surface currents and modelled Ekman currents at the surface and 15m depth (using ERA5 wind stress in REP and ERA5* in NRT). 1 hourly product, daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program).\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00327\n\n**References:**\n\n* Rio, M.-H., S. Mulet, and N. Picot: Beyond GOCE for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new insight into geostrophic and Ekman currents, Geophys. Res. Lett., 41, doi:10.1002/2014GL061773, 2014.\n", "doi": "10.48670/mds-00327", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-mynrt-015-003,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014": {"abstract": "The product MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014 is a reformatting and a simplified version of the CATDS L3 product called \u201c2Q\u201d or \u201cL2Q\u201d. it is an intermediate product, that provides, in daily files, SSS corrected from land-sea contamination and latitudinal bias, with/without rain freshening correction.\n\n**DOI (product):** \nhttps://doi.org/10.1016/j.rse.2016.02.061\n\n**References:**\n\n* Boutin, J., J. L. Vergely, S. Marchand, F. D'Amico, A. Hasson, N. Kolodziejczyk, N. Reul, G. Reverdin, and J. Vialard (2018), New SMOS Sea Surface Salinity with reduced systematic errors and improved variability, Remote Sensing of Environment, 214, 115-134. doi:https://doi.org/10.1016/j.rse.2018.05.022\n* Kolodziejczyk, N., J. Boutin, J.-L. Vergely, S. Marchand, N. Martin, and G. Reverdin (2016), Mitigation of systematic errors in SMOS sea surface salinity, Remote Sensing of Environment, 180, 164-177. doi:https://doi.org/10.1016/j.rse.2016.02.061\n", "doi": "10.1016/j.rse.2016.02.061", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,multi-year,multiobs-glo-phy-sss-l3-mynrt-015-014,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-salinity,sea-surface-salinity-error,sea-surface-salinity-qc,sea-surface-salinity-rain-corrected-error,sea-surface-salinity-sain-corrected,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2010-01-12T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015": {"abstract": "The product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called \u201cSMOS-OI\u201d. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information.\n\nKolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version: https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version: https://archimer.ifremer.fr/doc/00665/77702/\n\n**DOI (product):** \nhttps://doi.org/10.1175/JTECH-D-20-0093.1\n\n**References:**\n\n* Kolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/\n", "doi": "10.1175/JTECH-D-20-0093.1", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-sss-l4-my-015-015,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,sea-surface-temperature,sea-water-conservative-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2010-06-03T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "SSS SMOS/SMAP L4 OI - LOPS-v2023"}, "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013": {"abstract": "This product consits of daily global gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/8\u00b0 of resolution, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA\u2019s Soil Moisture Active Passive (SMAP) and ESA\u2019s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite SST information. The product was developed by the Consiglio Nazionale delle Ricerche (CNR) and includes 4 datasets:\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1D, which provides near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1M, which provides near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy-sss_my_multi_P1D, which provides multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy-sss_my_multi_P1M, which provides multi-year reprocessed (REP) monthly data \n\n**Product citation**: \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00051\n\n**References:**\n\n* Droghei, R., B. Buongiorno Nardelli, and R. Santoleri, 2016: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface. J. Atmos. Oceanic Technol. doi:10.1175/JTECH-D-15-0194.1.\n* Buongiorno Nardelli, B., R. Droghei, and R. Santoleri, 2016: Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data. Rem. Sens. Environ., doi:10.1016/j.rse.2015.12.052.\n* Droghei, R., B. Buongiorno Nardelli, and R. Santoleri, 2018: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993\u20132016), Front. Mar. Sci., 5(March), 1\u201313, doi:10.3389/fmars.2018.00084.\n* Sammartino, Michela, Salvatore Aronica, Rosalia Santoleri, and Bruno Buongiorno Nardelli. (2022). Retrieving Mediterranean Sea Surface Salinity Distribution and Interannual Trends from Multi-Sensor Satellite and In Situ Data, Remote Sensing 14, 2502: https://doi.org/10.3390/rs14102502.\n", "doi": "10.48670/moi-00051", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-s-surface-mynrt-015-013,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012": {"abstract": "You can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing. It consists of 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/8 degree regular grid and on 50 depth levels from the surface down to the bottom. The product includes 5 datasets: \n* cmems_obs-mob_glo_phy_nrt_0.125deg_P1D-m, which delivers near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy_nrt_0.125deg_P1M-m, which delivers near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy_my_0.125deg_P1D-m, which delivers multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy_my_0.125deg_P1M-m, which delivers multi-year reprocessed (REP) monthly data\n* cmems_obs-mob_glo_phy_mynrt_0.125deg-climatology-uncertainty_P1M-m, which delivers monthly static uncertainty data\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00052\n\n**References:**\n\n* Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845\u2013857.\n* Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77\u201380(0):70\u201381.\n", "doi": "10.48670/moi-00052", "instrument": null, "keywords": "coastal-marine-environment,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-tsuv-3d-mynrt-015-012,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "MULTIOBS_GLO_PHY_W_3D_REP_015_007": {"abstract": "You can find here the OMEGA3D observation-based quasi-geostrophic vertical and horizontal ocean currents developed by the Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4\u00b0 horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_REP_015_002 which corresponds to former version of MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012) and ERA-Interim surface fluxes. \n\n**DOI (product):** \nhttps://doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007\n\n**References:**\n\n* Buongiorno Nardelli, B. A Multi-Year Timeseries of Observation-Based 3D Horizontal and Vertical Quasi-Geostrophic Global Ocean Currents. Earth Syst. Sci. Data 2020, No. 12, 1711\u20131723. https://doi.org/10.5194/essd-12-1711-2020.\n", "doi": "10.25423/cmcc/multiobs_glo_phy_w_rep_015_007", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-w-3d-rep-015-007,northward-sea-water-velocity,numerical-model,oceanographic-geographical-features,satellite-observation,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-06T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D)"}, "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS NORTHWESTSHELF_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The North-West Shelf Multi Year Product (NWSHELF_MULTIYEAR_PHY_004_009) and the Analysis product (NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013).\nTwo parameters are included on this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\n**CONTEXT**\n\nThis domain comprises the North West European continental shelf where depths do not exceed 200m and deeper Atlantic waters to the North and West. For these deeper waters, the North-South temperature gradient dominates (Liu and Tanhua, 2021). Temperature over the continental shelf is affected also by the various local currents in this region and by the shallow depth of the water (Elliott et al., 1990). Atmospheric heat waves can warm the whole water column, especially in the southern North Sea, much of which is no more than 30m deep (Holt et al., 2012). Warm summertime water observed in the Norwegian trench is outflow heading North from the Baltic Sea and from the North Sea itself.\n\n**CMEMS KEY FINDINGS**\n\nThe 99th percentile SST product can be considered to represent approximately the warmest 4 days for the sea surface in Summer. Maximum anomalies for 2020 are up to 4oC warmer than the 1993-2019 average in the western approaches, Celtic and Irish Seas, English Channel and the southern North Sea. For the atmosphere, Summer 2020 was exceptionally warm and sunny in southern UK (Kendon et al., 2021), with heatwaves in June and August. Further north in the UK, the atmosphere was closer to long-term average temperatures. Overall, the 99th percentile SST anomalies show a similar pattern, with the exceptional warm anomalies in the south of the domain.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product)**\nhttps://doi.org/10.48670/moi-00273\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Elliott, A.J., Clarke, T., Li, ., 1990: Monthly distributions of surface and bottom temperatures in the northwest European shelf seas. Continental Shelf Research, Vol 11, no 5, pp 453-466, http://doi.org/10.1016/0278-4343(91)90053-9\n* Holt, J., Hughes, S., Hopkins, J., Wakelin, S., Holliday, P.N., Dye, S., Gonz\u00e1lez-Pola, C., Hj\u00f8llo, S., Mork, K., Nolan, G., Proctor, R., Read, J., Shammon, T., Sherwin, T., Smyth, T., Tattersall, G., Ward, B., Wiltshire, K., 2012: Multi-decadal variability and trends in the temperature of the northwest European continental shelf: A model-data synthesis. Progress in Oceanography, 96-117, 106, http://doi.org/10.1016/j.pocean.2012.08.001\n* Kendon, M., McCarthy, M., Jevrejeva, S., Matthews, A., Sparks, T. and Garforth, J. (2021), State of the UK Climate 2020. Int J Climatol, 41 (Suppl 2): 1-76. https://doi.org/10.1002/joc.7285\n* Liu, M., Tanhua, T., 2021: Water masses in the Atlantic Ocean: characteristics and distributions. Ocean Sci, 17, 463-486, http://doi.org/10.5194/os-17-463-2021\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00273", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northwestshelf-omi-tempsal-extreme-var-temp-mean-and-anomaly,numerical-model,oceanographic-geographical-features,temp-percentile99-anom,temp-percentile99-mean,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf Sea Surface Temperature extreme from Reanalysis"}, "NWSHELF_ANALYSISFORECAST_BGC_004_002": {"abstract": "The NWSHELF_ANALYSISFORECAST_BGC_004_002 is produced by a coupled physical-biogeochemical model, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated weekly, providing 10-day forecast of the main biogeochemical variables.\nProducts are provided as daily and monthly means.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00056", "doi": "10.48670/moi-00056", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,nwshelf-analysisforecast-bgc-004-002,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-05-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "NWSHELF_ANALYSISFORECAST_PHY_004_013": {"abstract": "The NWSHELF_ANALYSISFORECAST_PHY_004_013 is produced by a hydrodynamic model with tides, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated daily, providing 10-day forecast for temperature, salinity, currents, sea level and mixed layer depth.\nProducts are provided at quarter-hourly, hourly, daily de-tided (with Doodson filter), and monthly frequency.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00054", "doi": "10.48670/moi-00054", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,nwshelf-analysisforecast-phy-004-013,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "NWSHELF_ANALYSISFORECAST_WAV_004_014": {"abstract": "The NWSHELF_ANALYSISFORECAST_WAV_004_014 is produced by a wave model system based on MFWAV, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution forced by ECMWF wind data. The system assimilates significant wave height altimeter data and spectral data, and it is forced by currents provided by the [ ref t the physical system] ocean circulation system.\nThe product is updated twice a day, providing 10-day forecast of wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state, and wind-state, and swell components. \nProducts are provided at hourly frequency\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00055\n\n**References:**\n\n* The impact of ocean-wave coupling on the upper ocean circulation during storm events (Bruciaferri, D., Tonani, M., Lewis, H., Siddorn, J., Saulter, A., Castillo, J.M., Garcia Valiente, N., Conley, D., Sykes, P., Ascione, I., McConnell, N.) in Journal of Geophysical Research, Oceans, 2021, 126, 6. https://doi.org/10.1029/2021JC017343\n", "doi": "10.48670/moi-00055", "instrument": null, "keywords": "coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,none,north-west-shelf-seas,numerical-model,nwshelf-analysisforecast-wav-004-014,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-10-06T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast"}, "NWSHELF_MULTIYEAR_BGC_004_011": {"abstract": "**Short Description:**\n\nThe ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butenschön et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009). Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data.\n\nThe description of the model and its configuration, including the products validation is provided in the [CMEMS-NWS-QUID-004-011](http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-011.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually, providing a six-month extension of the time series. See [CMEMS-NWS-PUM-004-009_011](http://resources.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-009_011.pdf) for details.\n\n**Associated products:**\n\nThis model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009).\nAn analysis-forecast product is available from: [NWSHELF_MULTIYEAR_BGC_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00058\n\n**References:**\n\n* Ciavatta, S., Brewin, R. J. W., Sk\u00e1kala, J., Polimene, L., de Mora, L., Artioli, Y., & Allen, J. I. (2018). [https://doi.org/10.1002/2017JC013490 Assimilation of ocean\u2010color plankton functional types to improve marine ecosystem simulations]. Journal of Geophysical Research: Oceans, 123, 834\u2013854. https://doi.org/10.1002/2017JC013490\n", "doi": "10.48670/moi-00058", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,nwshelf-multiyear-bgc-004-011,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "NWSHELF_MULTIYEAR_PHY_004_009": {"abstract": "**Short Description:**\n\nThe ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution. \nThe ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [GLOBAL_REANALYSIS_PHY_001_026](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026) and at the Baltic boundary by the [BALTICSEA_REANALYSIS_PHY_003_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_PHY_003_011). The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. \n\nFurther details of the model, including the product validation are provided in the [CMEMS-NWS-QUID-004-009](http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-009.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually provinding six-month extension of the time series.\n\nSee [CMEMS-NWS-PUM-004-009_011](http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-009_011.pdf) for further details.\n\n**Associated products:**\n\nThis model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011). An analysis-forecast product is available from [NWSHELF_ANALYSISFORECAST_PHY_LR_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001).\nThe product is updated biannually provinding six-month extension of the time series.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00059", "doi": "10.48670/moi-00059", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,nwshelf-multiyear-phy-004-009,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "NWSHELF_REANALYSIS_WAV_004_015": {"abstract": "**Short description:**\n\nThis product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013). The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [ECMWF ERA-5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [CMEMS-NWS-PUM-004-015](http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-015.pdf) for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [CMEMS-NWS-QUID-004-015](http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-015.pdf).\nThe product is updated biannually provinding six-month extension of the time series.\n\n**Associated products:**\n\n[NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00060", "doi": "10.48670/moi-00060", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,none,north-west-shelf-seas,numerical-model,nwshelf-reanalysis-wav-004-015,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Wave Physics Reanalysis"}, "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products of region ARC are delivered in polar Lambertian Azimuthal Equal Area (LAEA) projection (EPSG:6931, EASE2). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-m\n*cmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-m\n*cmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-m\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00061\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00061", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-arc-bgc-hr-l3-nrt-009-201,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Region, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products of region ARC are delivered in polar Lambertian Azimuthal Equal Area (LAEA) projection (EPSG:6931, EASE2). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n\n**Dataset names: **\n*cmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00062\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00062", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-arc-bgc-hr-l4-nrt-009-207,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_ARC_BGC_L3_MY_009_123": {"abstract": "For the **Arctic** Ocean **Satellite Observations**, Italian National Research Council (CNR \u2013 Rome, Italy) is providing **Bio-Geo_Chemical (BGC)** products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the **\"multi\"** products and S3A & S3B only for the **\"OLCI\"** products.\n* Variables: Chlorophyll-a (**CHL**), Diffuse Attenuation (**KD490**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **4 km** (multi) or **300 m** (OLCI).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00292", "doi": "10.48670/moi-00292", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-arc-bgc-l3-my-009-123,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121": {"abstract": "For the **Arctic** Ocean **Satellite Observations**, Italian National Research Council (CNR \u2013 Rome, Italy) is providing **Bio-Geo_Chemical (BGC)** products.\n* Upstreams: OLCI-S3A & OLCI-S3B for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Suspended Matter (**SPM**), Diffuse Attenuation (**KD490**), Detrital and Dissolved Material Absorption Coef. (**ADG443_), Phytoplankton Absorption Coef. (**APH443_), Total Absorption Coef. (**ATOT443_) and Reflectance (**RRS_').\n\n* Temporal resolutions: **daily**, **monthly**.\n* Spatial resolutions: **300 meters** (olci).\n* Recent products are organized in datasets called Near Real Time (**NRT**).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00290", "doi": "10.48670/moi-00290", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-arc-bgc-l3-nrt-009-121,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_ARC_BGC_L4_MY_009_124": {"abstract": "For the **Arctic** Ocean **Satellite Observations**, Italian National Research Council (CNR \u2013 Rome, Italy) is providing **Bio-Geo_Chemical (BGC)** products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the **\"multi\"** products , and S3A & S3B only for the **\"OLCI\"** products.\n* Variables: Chlorophyll-a (**CHL**), Diffuse Attenuation (**KD490**)\n\n\n* Temporal resolutions: **monthly**.\n* Spatial resolutions: **4 km** (multi) or **300 meters** (OLCI).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00293", "doi": "10.48670/moi-00293", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-arc-bgc-l4-my-009-124,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton MY L4 daily climatology and monthly observations"}, "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122": {"abstract": "For the **Arctic** Ocean **Satellite Observations**, Italian National Research Council (CNR \u2013 Rome, Italy) is providing **Bio-Geo_Chemical (BGC)** products.\n* Upstreams: OLCI-S3A & OLCI-S3B for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**) and Diffuse Attenuation (**KD490**).\n\n* Temporal resolutions:**monthly**.\n* Spatial resolutions: **300 meters** (olci).\n* Recent products are organized in datasets called Near Real Time (**NRT**).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00291", "doi": "10.48670/moi-00291", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-arc-bgc-l4-nrt-009-122,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton and Transparency L4 NRT monthly observations"}, "OCEANCOLOUR_ATL_BGC_L3_MY_009_113": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **1 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00286", "doi": "10.48670/moi-00286", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-atl-bgc-l3-my-009-113,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **1 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00284", "doi": "10.48670/moi-00284", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-atl-bgc-l3-nrt-009-111,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_ATL_BGC_L4_MY_009_118": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and S3A & S3B only for the **\"olci\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: **1 km**.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"GlobColour\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00289", "doi": "10.48670/moi-00289", "instrument": null, "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-atl-bgc-l4-my-009-118,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and S3A & S3B only for the **\"olci\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: **1 km**.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"GlobColour\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00288", "doi": "10.48670/moi-00288", "instrument": null, "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-atl-bgc-l4-nrt-009-116,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_bal_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l3-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00079\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00079", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-bal-bgc-hr-l3-nrt-009-202,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00080\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00080", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-bal-bgc-hr-l4-nrt-009-208,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-08T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_BAL_BGC_L3_MY_009_133": {"abstract": "For the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv6) for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolution**: 1 km for **\"\"multi\"\"** (4 km for **\"\"pp\"\"**) and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BAL_BGC_L3_MY\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00296\n\n**References:**\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "doi": "10.48670/moi-00296", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-bal-bgc-l3-my-009-133,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131": {"abstract": "For the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n**Upstreams**: OLCI-S3A & S3B \n\n**Temporal resolution**: daily\n\n**Spatial resolution**: 300 meters \n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BAL_BGC_L3_NRT\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00294\n\n**References:**\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic Sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n", "doi": "10.48670/moi-00294", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-bal-bgc-l3-nrt-009-131,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-18T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_BAL_BGC_L4_MY_009_134": {"abstract": "For the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolutions**: monthly\n\n**Spatial resolution**: 1 km for **\"\"multi\"\"** and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BAL_BGC_L4_MY\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00308\n\n**References:**\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "doi": "10.48670/moi-00308", "instrument": null, "keywords": "baltic-sea,chl,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-bal-bgc-l4-my-009-134,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132": {"abstract": "For the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n**Upstreams**: OLCI-S3A & S3B \n\n**Temporal resolution**: monthly \n\n**Spatial resolution**: 300 meters \n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BAL_BGC_L4_NRT\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00295\n\n**References:**\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic Sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "doi": "10.48670/moi-00295", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-bal-bgc-l4-nrt-009-132,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations"}, "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided within 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_blk_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l3-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00086\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00086", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-blk-bgc-hr-l3-nrt-009-206,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00087\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00087", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-blk-bgc-hr-l4-nrt-009-212,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-02T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_BLK_BGC_L3_MY_009_153": {"abstract": "For the **Black Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and OLCI-S3A & S3B for the **\"olci\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolution**: 1 km for **\"multi\"** and 300 meters for **\"olci\"**\n\nTo find this product in the catalogue, use the search keyword **\"OCEANCOLOUR_BLK_BGC_L3_MY\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00303\n\n**References:**\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00303", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-blk-bgc-l3-my-009-153,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-16T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151": {"abstract": "For the **Black Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolutions**: 1 km for **\"\"multi\"\"** and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BLK_BGC_L3_NRT\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00301\n\n**References:**\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00301", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-blk-bgc-l3-nrt-009-151,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-29T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_BLK_BGC_L4_MY_009_154": {"abstract": "For the **Black Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated **gap-free** Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolutions**: monthly and daily (for **\"\"gap-free\"\"**, **\"\"pp\"\"** and climatology data)\n\n**Spatial resolution**: 1 km for **\"\"multi\"\"** (4 km for **\"\"pp\"\"**) and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BLK_BGC_L4_MY\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00304\n\n**References:**\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00304", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-blk-bgc-l4-my-009-154,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152": {"abstract": "For the **Black Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated **gap-free** Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for **\"\"multi**\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolutions**: monthly and daily (for **\"\"gap-free\"\"** and **\"\"pp\"\"** data)\n\n**Spatial resolutions**: 1 km for **\"\"multi\"\"** (4 km for **\"\"pp\"\"**) and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BLK_BGC_L4_NRT\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00302\n\n**References:**\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00302", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-blk-bgc-l4-nrt-009-152,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_103": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\"\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00280", "doi": "10.48670/moi-00280", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-glo-bgc-l3-my-009-103,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_107": {"abstract": "For the **Global** Ocean **Satellite Observations**, Brockmann Consult (BC) is providing **Bio-Geo_Chemical (BGC)** products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the **\"\"multi\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**, **monthly**.\n* Spatial resolutions: **4 km** (multi).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find these products in the catalogue, use the search keyword **\"\"ESA-CCI\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00282", "doi": "10.48670/moi-00282", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-glo-bgc-l3-my-009-107,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00278", "doi": "10.48670/moi-00278", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-glo-bgc-l3-nrt-009-101,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_104": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"\"cloud free\"\" product.\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\"\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00281", "doi": "10.48670/moi-00281", "instrument": null, "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-glo-bgc-l4-my-009-104,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_108": {"abstract": "For the **Global** Ocean **Satellite Observations**, Brockmann Consult (BC) is providing **Bio-Geo_Chemical (BGC)** products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the **\"\"multi\"\"** products.\n* Variables: Chlorophyll-a (**CHL**).\n\n* Temporal resolutions: **monthly**.\n* Spatial resolutions: **4 km** (multi).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find these products in the catalogue, use the search keyword **\"\"ESA-CCI\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00283", "doi": "10.48670/moi-00283", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-glo-bgc-l4-my-009-108,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour Plankton MY L4 monthly observations"}, "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and S3A & S3B only for the **\"olci\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"GlobColour\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00279", "doi": "10.48670/moi-00279", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-glo-bgc-l4-nrt-009-102,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": "proprietary", "missionStartDate": "2023-04-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_nws_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l3-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00107\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00107", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-ibi-bgc-hr-l3-nrt-009-204,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberic Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00108\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00108", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-ibi-bgc-hr-l4-nrt-009-210,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_ibi_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l3-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00109\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00109", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-hr-l3-nrt-009-205,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1-) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1M-v01+D19\n*cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00110\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00110", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-hr-l4-nrt-009-211,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_MED_BGC_L3_MY_009_143": {"abstract": "For the **Mediterranean Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for **\"multi**\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n* **_pp**_ with the Integrated Primary Production (PP)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and OLCI-S3A & S3B for the **\"olci\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolution**: 1 km for **\"multi\"** and 300 meters for **\"olci\"**\n\nTo find this product in the catalogue, use the search keyword **\"OCEANCOLOUR_MED_BGC_L3_MY\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00299\n\n**References:**\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Di Cicco A, Sammartino M, Marullo S and Santoleri R (2017) Regional Empirical Algorithms for an Improved Identification of Phytoplankton Functional Types and Size Classes in the Mediterranean Sea Using Satellite Data. Front. Mar. Sci. 4:126. doi: 10.3389/fmars.2017.00126\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00299", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,oceancolour-med-bgc-l3-my-009-143,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-16T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_MED_BGC_L3_NRT_009_141": {"abstract": "For the **Mediterranean Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for **\"\"multi**\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolutions**: 1 km for **\"\"multi\"\"** and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_MED_BGC_L3_NRT\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00297\n\n**References:**\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Di Cicco A, Sammartino M, Marullo S and Santoleri R (2017) Regional Empirical Algorithms for an Improved Identification of Phytoplankton Functional Types and Size Classes in the Mediterranean Sea Using Satellite Data. Front. Mar. Sci. 4:126. doi: 10.3389/fmars.2017.00126\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00297", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-l3-nrt-009-141,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-29T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_MED_BGC_L4_MY_009_144": {"abstract": "For the **Mediterranean Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated **gap-free** Chl concentration (to provide a \"cloud free\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and OLCI-S3A & S3B for the **\"olci\"** products\n\n**Temporal resolutions**: monthly and daily (for **\"gap-free\"** and climatology data)\n\n**Spatial resolution**: 1 km for **\"multi\"** and 300 meters for **\"olci\"**\n\nTo find this product in the catalogue, use the search keyword **\"OCEANCOLOUR_MED_BGC_L4_MY\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00300\n\n**References:**\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00300", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,oceancolour-med-bgc-l4-my-009-144,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_MED_BGC_L4_NRT_009_142": {"abstract": "For the **Mediterranean Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated **gap-free** Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for **\"\"multi**\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolutions**: monthly and daily (for **\"\"gap-free\"\"** and **\"\"pp\"\"** data)\n\n**Spatial resolutions**: 1 km for **\"\"multi\"\"** (4 km for **\"\"pp\"\"**) and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_MED_BGC_L4_NRT\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00298\n\n**References:**\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00298", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-l4-nrt-009-142,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00118\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00118", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-nws-bgc-hr-l3-nrt-009-203,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf Region, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00119\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00119", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,north-west-shelf-seas,oceancolour-nws-bgc-hr-l4-nrt-009-209,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index": {"abstract": "**DEFINITION**\n\nThe Black Sea Rim Current index (BSRCI) reflects the intensity of the Rim current, which is a main feature of the Black Sea circulation, a basin scale cyclonic current. The index was computed using sea surface current speed averaged over two areas of intense currents based on reanalysis data. The areas are confined between the 200 and 1800 m isobaths in the northern section 33-39E (from the Caucasus coast to the Crimea Peninsula), and in the southern section 31.5-35E (from Sakarya region to near Sinop Peninsula). Thus, three indices were defined: one for the northern section (BSRCIn), for the southern section (BSRCIs) and an average for the entire basin (BSRCI).\nBSRCI=(V \u0305_ann-V \u0305_cl)/V \u0305_cl \nwhere V \u0305 denotes the representative area average, the \u201cann\u201d denotes the annual mean for each individual year in the analysis, and \u201ccl\u201d indicates the long-term mean over the whole period 1993-2020. In general, BSRCI is defined as the relative annual anomaly from the long-term mean speed. An index close to zero means close to the average conditions a positive index indicates that the Rim current is more intense than average, or negative - if it is less intense than average. In other words, positive BSRCI would mean higher circumpolar speed, enhanced baroclinicity, enhanced dispersion of pollutants, less degree of exchange between open sea and coastal areas, intensification of the heat redistribution, etc.\nThe BSRCI is introduced in the fifth issue of the Ocean State Report (von Schuckmann et al., 2021). The Black Sea Physics Reanalysis (BLKSEA_REANALYSIS_PHYS_007_004) has been used as a data base to build the index. Details on the products are delivered in the PUM and QUID of this OMI.\n\n**CONTEXT**\n\nThe Black Sea circulation is driven by the regional winds and large freshwater river inflow in the north-western part (including the main European rivers Danube, Dnepr and Dnestr). The major cyclonic gyre encompasses the sea, referred to as Rim current. It is quasi-geostrophic and the Sverdrup balance approximately applies to it. \nThe Rim current position and speed experiences significant interannual variability (Stanev and Peneva, 2002), intensifying in winter due to the dominating severe northeastern winds in the region (Stanev et al., 2000). Consequently, this impacts the vertical stratification, Cold Intermediate Water formation, the biological activity distribution and the coastal mesoscale eddies\u2019 propagation along the current and their evolution. The higher circumpolar speed leads to enhanced dispersion of pollutants, less degree of exchange between open sea and coastal areas, enhanced baroclinicity, intensification of the heat redistribution which is important for the winter freezing in the northern zones (Simonov and Altman, 1991). Fach (2015) finds that the anchovy larval dispersal in the Black Sea is strongly controlled at the basin scale by the Rim Current and locally - by mesoscale eddies. \nSeveral recent studies of the Black Sea pollution claim that the understanding of the Rim Current behavior and how the mesoscale eddies evolve would help to predict the transport of various pollution such as oil spills (Korotenko, 2018) and floating marine litter (Stanev and Ricker, 2019) including microplastic debris (Miladinova et al., 2020) raising a serious environmental concern today. \nTo summarize, the intensity of the Black Sea Rim Current could give valuable integral measure for a great deal of physical and biogeochemical processes manifestation. Thus our objective is to develop a comprehensive index reflecting the annual mean state of the Black Sea general circulation to be used by policy makers and various end users. \n\n**CMEMS KEY FINDINGS**\n\nThe Black Sea Rim Current Index is defined as the relative annual anomaly of the long-term mean speed. The BSRCI value characterizes the annual circulation state: a value close to zero would mean close to average conditions, positive value indicates enhanced circulation, and negative value \u2013 weaker circulation than usual. The time-series of the BSRCI suggest that the Black Sea Rim current speed varies within ~30% in the period 1993-2020 with a positive trend of ~0.1 m/s/decade. In the years 2005 and 2014 there is evidently higher mean velocity, and on the opposite end are the years \u20132004, 2013 and 2016. The time series of the BSRCI gives possibility to check the relationship with the wind vorticity and validate the Sverdrup balance hypothesis. \n\n**Figure caption**\n\nTime series of the Black Sea Rim Current Index (BSRCI) at the north section (BSRCIn), south section (BSRCIs), the average (BSRCI) and its tendency for the period 1993-2020.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00326\n\n**References:**\n\n* Capet, A., A. Barth, J.-M. Beckers, and M. Gr\u00e9goire (2012), Interannual variability of Black Sea\u2019s hydrodynamics and connection to atmospheric patterns, Deep-Sea Res. Pt. II, 77 \u2013 80, 128\u2013142, doi:10.1016/j.dsr2.2012.04.010\n* Fach, B., (2015), Modeling the Influence of Hydrodynamic Processes on Anchovy Distribution and Connectivity in the Black Sea, Turkish Journal of Fisheries and Aquatic Sciences 14: 1-2, doi: 10.4194/1303-2712-v14_2_06\n* Ivanov V.A., Belokopytov V.N. (2013) Oceanography of the Black Sea. Editorial publishing board of Marine Hydrophysical Institute, 210 p, Printed by ECOSY-Gidrofizika, Sevastopol Korotaev, G., T. Oguz, A. Nikiforov, and C. Koblinsky. Seasonal, interannual, and mesoscale variability of the Black Sea upper layer circulation derived from altimeter data. Journal of Geophysical Research (Oceans). 108. C4. doi: 10. 1029/2002JC001508, 2003\n* Korotenko KA. Effects of mesoscale eddies on behavior of an oil spill resulting from an accidental deepwater blowout in the Black Sea: an assessment of the environmental impacts. PeerJ. 2018 Aug 29;6:e5448. doi: 10.7717/peerj.5448. PMID: 30186680; PMCID: PMC6119461.\n* Kubryakov, A. A., and S.V. Stanichny (2015), Seasonal and interannual variability of the Black Sea eddies and its dependence on characteristics of the large-scale circulation, Deep-Sea Res. Pt. I, 97, 80-91, https://doi.org/10.1016/j.dsr.2014.12.002\n* Miladinova S., A. Stips, D. Macias Moy, E. Garcia-Gorriz, (2020a) Pathways and mixing of the north western river waters in the Black Sea Estuarine, Coastal and Shelf Science, Volume 236, 5 May 2020, https://doi\n", "doi": "10.48670/mds-00326", "instrument": null, "keywords": "black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-circulation-boundary-blksea-rim-current-index,rim-current-intensity-index,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Rim Current Intensity Index"}, "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged": {"abstract": "**DEFINITION**\n\nThe indicator of the Kuroshio extension phase variations is based on the standardized high frequency altimeter Eddy Kinetic Energy (EKE) averaged in the area 142-149\u00b0E and 32-37\u00b0N and computed from the DUACS (https://duacs.cls.fr) delayed-time (reprocessed version DT-2021, CMEMS SEALEVEL_GLO_PHY_L4_MY_008_047, including \u201cmy\u201d (multi-year) & \u201cmyint\u201d (multi-year interim) datasets) and near real-time (CMEMS SEALEVEL_GLO_PHY_L4_NRT _008_046) altimeter sea level gridded products. The change in the reprocessed version (previously DT-2018) and the extension of the mean value of the EKE (now 27 years, previously 20 years) induce some slight changes not impacting the general variability of the Kuroshio extension (correlation coefficient of 0.988 for the total period, 0.994 for the delayed time period only). \n\n**CONTEXT**\n\nThe Kuroshio Extension is an eastward-flowing current in the subtropical western North Pacific after the Kuroshio separates from the coast of Japan at 35\u00b0N, 140\u00b0E. Being the extension of a wind-driven western boundary current, the Kuroshio Extension is characterized by a strong variability and is rich in large-amplitude meanders and energetic eddies (Niiler et al., 2003; Qiu, 2003, 2002). The Kuroshio Extension region has the largest sea surface height variability on sub-annual and decadal time scales in the extratropical North Pacific Ocean (Jayne et al., 2009; Qiu and Chen, 2010, 2005). Prediction and monitoring of the path of the Kuroshio are of huge importance for local economies as the position of the Kuroshio extension strongly determines the regions where phytoplankton and hence fish are located. Unstable (contracted) phase of the Kuroshio enhance the production of Chlorophyll (Lin et al., 2014).\n\n**CMEMS KEY FINDINGS**\n\nThe different states of the Kuroshio extension phase have been presented and validated by (Bessi\u00e8res et al., 2013) and further reported by Dr\u00e9villon et al. (2018) in the Copernicus Ocean State Report #2. Two rather different states of the Kuroshio extension are observed: an \u2018elongated state\u2019 (also called \u2018strong state\u2019) corresponding to a narrow strong steady jet, and a \u2018contracted state\u2019 (also called \u2018weak state\u2019) in which the jet is weaker and more unsteady, spreading on a wider latitudinal band. When the Kuroshio Extension jet is in a contracted (elongated) state, the upstream Kuroshio Extension path tends to become more (less) variable and regional eddy kinetic energy level tends to be higher (lower). In between these two opposite phases, the Kuroshio extension jet has many intermediate states of transition and presents either progressively weakening or strengthening trends. In 2018, the indicator reveals an elongated state followed by a weakening neutral phase since then.\n\n**Figure caption**\n\nStandardized Eddy Kinetic Energy over the Kuroshio region (following Bessi\u00e8res et al., 2013) Blue shaded areas correspond to well established strong elongated states periods, while orange shaded areas fit weak contracted states periods. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) completed by DUACS near Real Time (\u201cnrt\u201d) sea level multi-mission gridded products. The vertical red line shows the date of the transition between \u201cmyint\u201d and \u201cnrt\u201d products used.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00222\n\n**References:**\n\n* Bessi\u00e8res, L., Rio, M.H., Dufau, C., Boone, C., Pujol, M.I., 2013. Ocean state indicators from MyOcean altimeter products. Ocean Sci. 9, 545\u2013560. https://doi.org/10.5194/os-9-545-2013\n* Dr\u00e9villon, M., Legeais, J.-F., Peterson, A., Zuo, H., Rio, M.-H., Drillet, Y., Greiner, E., 2018. Western boundary currents. J. Oper. Oceanogr., Copernicus Marine Service Ocean State Report Issue 2, s60\u2013s65. https://doi.org/10.1080/1755876X.2018.1489208\n* Jayne, S.R., Hogg, N.G., Waterman, S.N., Rainville, L., Donohue, K.A., Randolph Watts, D., Tracey, K.L., McClean, J.L., Maltrud, M.E., Qiu, B., Chen, S., Hacker, P., 2009. The Kuroshio Extension and its recirculation gyres. Deep Sea Res. Part Oceanogr. Res. Pap. 56, 2088\u20132099. https://doi.org/10.1016/j.dsr.2009.08.006\n* Kelly, K.A., Small, R.J., Samelson, R.M., Qiu, B., Joyce, T.M., Kwon, Y.-O., Cronin, M.F., 2010. Western Boundary Currents and Frontal Air\u2013Sea Interaction: Gulf Stream and Kuroshio Extension. J. Clim. 23, 5644\u20135667. https://doi.org/10.1175/2010JCLI3346.1\n* Niiler, P.P., Maximenko, N.A., Panteleev, G.G., Yamagata, T., Olson, D.B., 2003. Near-surface dynamical structure of the Kuroshio Extension. J. Geophys. Res. Oceans 108. https://doi.org/10.1029/2002JC001461\n* Qiu, B., 2003. Kuroshio Extension Variability and Forcing of the Pacific Decadal Oscillations: Responses and Potential Feedback. J. Phys. Oceanogr. 33, 2465\u20132482. https://doi.org/10.1175/2459.1\n* Qiu, B., 2002. The Kuroshio Extension System: Its Large-Scale Variability and Role in the Midlatitude Ocean-Atmosphere Interaction. J. Oceanogr. 58, 57\u201375. https://doi.org/10.1023/A:1015824717293\n* Qiu, B., Chen, S., 2010. Eddy-mean flow interaction in the decadally modulating Kuroshio Extension system. Deep Sea Res. Part II Top. Stud. Oceanogr., North Pacific Oceanography after WOCE: A Commemoration to Nobuo Suginohara 57, 1098\u20131110. https://doi.org/10.1016/j.dsr2.2008.11.036\n* Qiu, B., Chen, S., 2005. Variability of the Kuroshio Extension Jet, Recirculation Gyre, and Mesoscale Eddies on Decadal Time Scales. J. Phys. Oceanogr. 35, 2090\u20132103. https://doi.org/10.1175/JPO2807.1\n", "doi": "10.48670/moi-00222", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-circulation-boundary-pacific-kuroshio-phase-area-averaged,satellite-observation,specific-turbulent-kinetic-energy-of-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Kuroshio Phase from Observations Reprocessing"}, "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean": {"abstract": "**DEFINITION**\n\nThis ocean monitoring indicator (OMI) provides a time series of Meridional Overturning Circulation (MOC) Strength in density coordinates, area-averaged and calculated for the period from 1993 to the most recent year with the availability of reanalysis data in the Black Sea (BS). It contains 1D (time dimension) maximum MOC data computed from the Black Sea Reanalysis (BLK-REA; BLKSEA_MULTIYEAR_PHY_007_004) (Ilicak et al., 2022). The MOC is calculated by summing the meridional transport provided by the Copernicus Marine BLK-REA within density bins. The Black Sea MOC indicator represents the maximum MOC value across the basin for a density range between 22.45 and 23.85 kg/m\u00b3, which corresponds approximately to a depth interval of 25 to 80 m. To understand the overturning circulation of the Black Sea, we compute the residual meridional overturning circulation in density space. Residual overturning as a function of latitude (y) and density (\u03c3 \u0305) bins can be computed as follows:\n\u03c8^* (y,\u03c3 \u0305 )=-1/T \u222b_(t_0)^(t_1)\u2592\u222b_(x_B1)^(x_B2)\u2592\u3016\u222b_(-H)^0\u2592H[\u03c3 \u0305-\u03c3(x,y,z,t)] \u00d7\u03bd(x,y,z,t)dzdxdt,\u3017\nwhere H is the Heaviside function and \u03bd is the meridional velocity. We used 100 \u03c3_2 (potential density anomaly with reference pressure of 2000 dbar) density bins to remap the mass flux fields.\n\n**CONTEXT**\n\nThe BS meridional overturning circulation (BS-MOC) is a clockwise circulation in the northern part up to 150 m connected to cold intermediate layer (CIL) and an anticlockwise circulation in the southern part that could be connected to the influence of the Mediterranean Water inflow into the BS. In contrast to counterparts observed in the deep Atlantic and Mediterranean overturning circulations, the BS-MOC is characterized by shallowness and relatively low strength. However, its significance lies in its capacity to monitor the dynamics and evolution of the CIL which is crucial for the ventilation of the subsurface BS waters. The monitoring of the BS-MOC evolution from the BLK-REA can support the understanding how the CIL formation is affected due to climate change. The study of Black Sea MOC is relatively new. For more details, see Ilicak et al., (2022).\n\n**KEY FINDINGS**\n\nThe MOC values show a significant decline from 1994 to 2009, corresponding to the reduction in the CIL during that period. However, after 2010, the MOC in the Black Sea increased from 0.07 Sv (1 Sv = 106 m3/s) to 0.10 Sv. The CIL has nearly disappeared in recent years, as discussed by Stanev et al. (2019) and Lima et al. (2021) based on observational data and reanalysis results. The opposite pattern observed since 2010 suggests that mechanisms other than the CIL may be influencing the Black Sea MOC.\nFor the OMI we have used an updated version of the reanalysis (version E4R1) which has a different spinup compared to the OSR6 (version E3R1).\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00349\n\n**References:**\n\n* Ilicak, M., Causio, S., Ciliberti, S., Coppini, G., Lima, L., Aydogdu, A., Azevedo, D., Lecci, R., Cetin, D. U., Masina, S., Peneva, E., Gunduz, M., Pinardi, N. (2022). The Black Sea overturning circulation and its indicator of change. In: Copernicus Ocean State Report, issue 6, Journal of Operational Oceanography, 15:sup1, s64:s71; DOI: doi.org/10.1080/1755876X.2022.2095169\n* Lima, L., Ciliberti, S.A., Aydo\u011fdu, A., Masina, S., Escudier, R., Cipollone, A., Azevedo, D., Causio, S., Peneva, E., Lecci, R., Clementi, E., Jansen, E., Ilicak, M., Cret\u00ec, S., Stefanizzi, L., Palermo, F., Coppini, G. (2021). Climate Signals in the Black Sea From a Multidecadal Eddy-Resolving Reanalysis. Front. Mar. Sci. 8:710973. doi: 10.3389/fmars.2021.710973\n* Stanev, E. V., Peneva, E., Chtirkova, B. (2019). Climate change and regional ocean water mass disappearance: case of the Black Sea. J. Geophys. Res. Oceans 124, 4803\u20134819. doi: 10.1029/2019JC015076\n", "doi": "10.48670/mds-00349", "instrument": null, "keywords": "black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,ocean-meridional-overturning-streamfunction,oceanographic-geographical-features,omi-circulation-moc-blksea-area-averaged-mean,s,sla,t,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Overturning Circulation Index from Reanalysis"}, "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean": {"abstract": "**DEFINITION**\n\nTime mean meridional Eulerian streamfunctions are computed using the velocity field estimate provided by the Copernicus Marine Mediterranean Sea reanalysis over the last 35 years (1987\u20132021). The Eulerian meridional streamfunction is evaluated by integrating meridional velocity daily data first in a vertical direction, then in a meridional direction, and finally averaging over the reanalysis period.\nThe Mediterranean overturning indices are derived for the eastern and western Mediterranean Sea by computing the annual streamfunction in the two areas separated by the Strait of Sicily around 36.5\u00b0N, and then considering the associated maxima. \nIn each case a geographical constraint focused the computation on the main region of interest. For the western index, we focused on deep-water formation regions, thus excluding both the effect of shallow physical processes and the Gibraltar net inflow. For the eastern index, we investigate the Levantine and Cretan areas corresponding to the strongest meridional overturning cell locations, thus only a zonal constraint is defined.\nTime series of annual mean values is provided for the Mediterranean Sea using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021).\nMore details can be found in the Copernicus Marine Ocean State Report issue 4 (OSR4, von Schuckmann et al., 2020) Section 2.4 (Lyubartsev et al., 2020).\n\n**CONTEXT**\n\nThe western and eastern Mediterranean clockwise meridional overturning circulation is connected to deep-water formation processes. The Mediterranean Sea 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021) is used to show the interannual variability of the Meridional Overturning Index. Details on the product are delivered in the PUM and QUID of this OMI. \nThe Mediterranean Meridional Overturning Index is defined here as the maxima of the clockwise cells in the eastern and western Mediterranean Sea and is associated with deep and intermediate water mass formation processes that occur in specific areas of the basin: Gulf of Lion, Southern Adriatic Sea, Cretan Sea and Rhodes Gyre (Pinardi et al., 2015).\nAs in the global ocean, the overturning circulation of the western and eastern Mediterranean are paramount to determine the stratification of the basins (Cessi, 2019). In turn, the stratification and deep water formation mediate the exchange of oxygen and other tracers between the surface and the deep ocean (e.g., Johnson et al., 2009; Yoon et al., 2018). In this sense, the overturning indices are potential gauges of the ecosystem health of the Mediterranean Sea, and in particular they could instruct early warning indices for the Mediterranean Sea to support the Sustainable Development Goal (SDG) 13 Target 13.3.\n\n**CMEMS KEY FINDINGS**\n\nThe western and eastern Mediterranean overturning indices (WMOI and EMOI) are synthetic indices of changes in the thermohaline properties of the Mediterranean basin related to changes in the main drivers of the basin scale circulation. The western sub-basin clockwise overturning circulation is associated with the deep-water formation area of the Gulf of Lion, while the eastern clockwise meridional overturning circulation is composed of multiple cells associated with different intermediate and deep-water sources in the Levantine, Aegean, and Adriatic Seas. \nOn average, the EMOI shows higher values than the WMOI indicating a more vigorous overturning circulation in eastern Mediterranean. The difference is mostly related to the occurrence of the eastern Mediterranean transient (EMT) climatic event, and linked to a peak of the EMOI in 1992. In 1999, the difference between the two indices started to decrease because EMT water masses reached the Sicily Strait flowing into the western Mediterranean Sea (Schroeder et al., 2016). The western peak in 2006 is discussed to be linked to anomalous deep-water formation during the Western Mediterranean Transition (Smith, 2008; Schroeder et al., 2016). Thus, the WMOI and EMOI indices are a useful tool for long-term climate monitoring of overturning changes in the Mediterranean Sea. \n\n**Figure caption**\n\nTime series of Mediterranean overturning indices [Sverdrup] calculated from the annual average of the meridional streamfunction over the period 1987 to 2021. Blue: Eastern Mediterranean Overturning Index (lat<36.5\u00b0N); Red: Western Mediterranean Overturning Index (lat\u226540\u00b0N, z>300m). Product used: MEDSEA_MULTIYEAR_PHY_006_004.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00317\n\n**References:**\n\n* Cessi, P. 2019. The global overturning circulation. Ann Rev Mar Sci. 11:249\u2013270. DOI:10.1146/annurev-marine- 010318-095241. Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., Omar, M., Masina, S., Coppini, G., Pinardi, N. 2021. A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, Vol.9, pp.1060, DOI:10.3389/feart.2021.702285.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) set. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Gertman, I., Pinardi, N., Popov, Y., Hecht, A. 2006. Aegean Sea water masses during the early stages of the eastern Mediterranean climatic Transient (1988\u20131990). J Phys Oceanogr. 36(9):1841\u20131859. DOI:10.1175/JPO2940.1.\n* Johnson, K.S., Berelson, W.M., Boss, E.S., Chase, Z., Claustre, H., Emerson, S.R., Gruber, N., Ko\u0308rtzinger, A., Perry, M.J., Riser, S.C. 2009. Observing biogeochemical cycles at global scales with profiling floats and gliders: prospects for a global array. Oceanography. 22:216\u2013225. DOI:10.5670/oceanog. 2009.81.\n* Lyubartsev, V., Borile, F., Clementi, E., Masina, S., Drudi, M/. Coppini, G., Cessi, P., Pinardi, N. 2020. Interannual variability in the Eastern and Western Mediterranean Overturning Index. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097.\n* Pinardi, N., Cessi, P., Borile, F., Wolfe, C.L.P. 2019. The Mediterranean Sea overturning circulation. J Phys Oceanogr. 49:1699\u20131721. DOI:10.1175/JPO-D-18-0254.1.\n* Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P., Tonani, M., Lyubartsev, V., Dobricic, S., Bonaduce, A. 2015. Mediterranean Sea large-scale, low-frequency ocean variability and water mass formation rates from 1987 to 2007: a retrospective analysis. Prog Oceanogr. 132:318\u2013332. DOI:10.1016/j.pocean.2013.11.003.\n* Roether, W., Klein, B., Hainbucher, D. 2014. Chap 6. The eastern Mediterranean transient. In: GL Eusebi Borzelli, M Gacic, P Lionello, P Malanotte-Rizzoli, editors. The Mediterranean Sea. American Geophysical Union (AGU); p. 75\u201383. DOI:10.1002/9781118847572.ch6.\n* Roether, W., Manca, B.B., Klein, B., Bregant, D., Georgopoulos, D., Beitzel, V., Kovac\u030cevic\u0301, V., Luchetta, A. 1996. Recent changes in the eastern Mediterranean deep waters. Science. 271:333\u2013335. DOI:10.1126/science.271.5247.333.\n* Schroeder, K., Chiggiato, J., Bryden, H., Borghini, M., Ismail, S.B. 2016. Abrupt climate shift in the western Mediterranean Sea. Sci Rep. 6:23009. DOI:10.1038/srep23009.\n* Smith, R.O., Bryden, H.L., Stansfield, K. 2008. Observations of new western Mediterranean deep water formation using Argo floats 2004-2006. Ocean Science, 4 (2), 133-149.\n* Von Schuckmann, K. et al. 2020. Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, DOI: 10.1080/1755876X.2020.1785097.\n* Yoon, S., Chang, K., Nam, S., Rho, T.K., Kang, D.J., Lee, T., Park, K.A., Lobanov, V., Kaplunenko, D., Tishchenko, P., Kim, K.R. 2018. Re-initiation of bottom water formation in the East Sea (Japan Sea) in a warming world. Sci Rep. 8:1576. DOI:10. 1038/s41598-018-19952-4.\n", "doi": "10.48670/mds-00317", "instrument": null, "keywords": "coastal-marine-environment,in-situ-ts-profiles,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,ocean-meridional-overturning-streamfunction,oceanographic-geographical-features,omi-circulation-moc-medsea-area-averaged-mean,sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1987-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Meridional Overturning Circulation Index from Reanalysis"}, "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged": {"abstract": "**DEFINITION**\n\nNet (positive minus negative) volume transport of Atlantic Water through the sections (see Figure 1): Faroe Shetland Channel (Water mass criteria, T > 5 \u00b0C); Barents Sea Opening (T > 3 \u00b0C) and the Fram Strait (T > 2 \u00b0C). Net volume transport of Overflow Waters (\u03c3\u03b8 >27.8 kg/m3) exiting from the Nordic Seas to the North Atlantic via the Denmark Strait and Faroe Shetland Channel. For further details, see Ch. 3.2 in von Schuckmann et al. (2018).\n\n**CONTEXT**\n\nThe poleward flow of relatively warm and saline Atlantic Water through the Nordic Seas to the Arctic Basin, balanced by the overflow waters exiting the Nordic Seas, governs the exchanges between the North Atlantic and the Arctic as well as the distribution of oceanic heat within the Arctic (e.g., Mauritzen et al., 2011; Rudels, 2012). Atlantic Water transported poleward has been found to significantly influence the sea-ice cover in the Barents Sea (Sand\u00f8 et al., 2010; \u00c5rthun et al., 2012; Onarheim et al., 2015) and near Svalbard (Piechura and Walczowski, 2009). Furthermore, Atlantic Water flow through the eastern Nordic seas and its associated heat loss and densification are important factors for the formation of overflow waters in the region (Mauritzen, 1996; Eldevik et al., 2009). These overflow waters together with those generated in the Arctic, exit the Greenland Scotland Ridge, which further contribute to the North Atlantic Deep Water (Dickson and Brown, 1994) and thus play an important role in the Atlantic Meridional Overturning Circulation (Eldevik et al., 2009; Ch. 2.3 in von Schuckmann et al., 2016). In addition to the transport of heat, the Atlantic Water also transports nutrients and zooplankton (e.g., Sundby, 2000), and it carries large amounts of ichthyoplankton of commercially important species, such as Arcto-Norwegian cod (Gadus morhua) and Norwegian spring-spawning herring (Clupea harengus) along the Norwegian coast. The Atlantic Water flow thus plays an integral part in defining both the physical and biological border between the boreal and Arctic realm. Variability of Atlantic Water flow to the Barents Sea has been found to move the position of the ice edge (Onarheim et al., 2015) as well as habitats of various species in the Barents Sea ecosystem (Fossheim et al., 2015).\n\n**CMEMS KEY FINDINGS**\n\nThe flow of Atlantic Water through the F\u00e6r\u00f8y-Shetland Channel amounts to 2.7 Sv (Berx et al., 2013). The corresponding model-based estimate was 2.5 Sv for the period 1993-2021. \nIn the Barents Sea Opening, the model indicates a long-term average net Atlantic Water inflow of 2.2 Sv, as compared with the long-term estimate from observations of 1.8 Sv (Smedsrud et al., 2013).\nIn the Fram Strait, the model data indicates a positive trend in the Atlantic Water transport to the Arctic. This trend may be explained by increased temperature in the West Spitsbergen Current during the period 2005-2010 (e.g., Walczowski et al., 2012), which caused a larger fraction of the water mass to be characterized as Atlantic Water (T > 2 \u00b0C).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00189\n\n**References:**\n\n* Berx B. Hansen B, \u00d8sterhus S, Larsen KM, Sherwin T, Jochumsen K. 2013. Combining in situ measurements and altimetry to estimate volume, heat and salt transport variability through the F\u00e6r\u00f8y-Shetland Channel. Ocean Sci. 9, 639-654\n* Dickson RR, Brown J. 1994. The production of North-Atlantic deep-water \u2013 sources, rates, and pathways. J Geophys Res Oceans. 99(C6), 12319-12341\n* Eldevik T, Nilsen JE\u00d8, Iovino D, Olsson KA, Sand\u00f8 AB, Drange H. 2009. Observed sources and variability of Nordic seas overflow. Nature Geosci. 2(6), 405-409\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan M.M, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat Climate Change. 5, 673-678.\n* Mauritzen C. 1996. Production of dense overflow waters feeding the North Atlantic across the Greenland-Scotland Ridge. 1. Evidence for a revised circulation scheme. Deep-Sea Res Part I. 43(6), 769-806\n* Mauritzen C, Hansen E, Andersson M, Berx B, Beszczynzka-M\u00f6ller A, Burud I, Christensen KH, Debernard J, de Steur L, Dodd P, et al. 2011. Closing the loop \u2013 Approaches to monitoring the state of the Arctic Mediterranean during the International Polar Year 2007-2008. Prog Oceanogr. 90, 62-89\n* Onarheim IH, Eldevik T, \u00c5rthun M, Ingvaldsen RB, Smedsrud LH. 2015. Skillful prediction of Barents Sea ice cover. Geophys Res Lett. 42(13), 5364-5371\n* Raj RP, Johannessen JA, Eldevik T, Nilsen JE\u00d8, Halo I. 2016. Quantifying mesoscale eddies in the Lofoten basin. J Geophys Res Oceans. 121. doi:10.1002/2016JC011637\n* Rudels B. 2012. Arctic Ocean circulation and variability \u2013 advection and external forcing encounter constraints and local processes. Ocean Sci. 8(2), 261-286\n* Sand\u00f8, A.B., J.E.\u00d8. Nilsen, Y. Gao and K. Lohmann, 2010: Importance of heat transport and local air-sea heat fluxes for Barents Sea climate variability. J Geophys Res. 115, C07013\n* von Schuckmann K, et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. J Oper Oceanogr. 9, 235-320\n* von Schuckmann K. 2018. Copernicus Marine Service Ocean State Report, J Oper Oceanogr. 11, sup1, S1-S142. Smedsrud LH, Esau I, Ingvaldsen RB, Eldevik T, Haugan PM, Li C, Lien VS, Olsen A, Omar AM, Otter\u00e5 OH, Risebrobakken B, Sand\u00f8 AB, Semenov VA, Sorokina SA. 2013. The role of the Barents Sea in the climate system. Rev Geophys. 51, 415-449\n* Sundby, S., 2000. Recruitment of Atlantic cod stocks in relation to temperature and advection of copepod populations. Sarsia. 85, 277-298.\n* Walczowski W, Piechura J, Goszczko I, Wieczorek P. 2012. Changes in Atlantic water properties: an important factor in the European Arctic marine climate. ICES J Mar Sys. 69(5), 864-869.\n* Piechura J, Walczowski W. 2009. Warming of the West Spitsbergen Current and sea ice north of Svalbard. Oceanol. 51(2), 147-164\n* \u00c5rthun, M., Eldevik, T., Smedsrud, L.H., Skagseth, \u00d8., Ingvaldsen, R.B., 2012. Quantifying the Influence of Atlantic Heat on Barents Sea Ice Variability and Retreat. J. Climate. 25, 4736-4743.\n", "doi": "10.48670/moi-00189", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,omi-circulation-voltrans-arctic-averaged,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nordic Seas Volume Transports from Reanalysis"}, "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies": {"abstract": "**DEFINITION**\n\nThe product OMI_IBI_CURRENTS_VOLTRANS_section_integrated_anomalies is defined as the time series of annual mean volume transport calculated across a set of vertical ocean sections. These sections have been chosen to be representative of the temporal variability of various ocean currents within the IBI domain.\nThe currents that are monitored include: transport towards the North Sea through Rockall Trough (RTE) (Holliday et al., 2008; Lozier and Stewart, 2008), Canary Current (CC) (Knoll et al. 2002, Mason et al. 2011), Azores Current (AC) (Mason et al., 2011), Algerian Current (ALG) (Tintor\u00e9 et al, 1988; Benzohra and Millot, 1995; Font et al., 1998), and net transport along the 48\u00baN latitude parallel (N48) (see OMI Figure).\nTo provide ensemble-based results, four Copernicus products have been used. Among these products are three reanalysis products (GLO-REA, IBI-REA and MED-REA) and one product obtained from reprocessed observations (GLO-ARM).\n\u2022\tGLO-REA: GLOBAL_MULTIYEAR_PHY_001_030 (Reanalysis)\n\u2022\tIBI-REA: IBI_MULTIYEAR_PHY_005_002 (Reanalysis)\n\u2022\tMED-REA: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (Reprocessed observations)\n\u2022\tMED-REA: MEDSEA_MULTIYEAR_PHY_006_004MEDSEA_MULTIYEAR_PHY_006_004 (Reanalysis)\nThe time series comprises the ensemble mean (blue line), the ensemble spread (grey shaded area), and the mean transport with the sign reversed (red dashed line) to indicate the threshold of anomaly values that would entail a reversal of the current transport. Additionally, the analysis of trends in the time series at the 95% confidence interval is included in the bottom right corner of each diagram.\nDetails on the product are given in the corresponding Product User Manual (de Pascual-Collar et al., 2024a) and QUality Information Document (de Pascual-Collar et al., 2024b) as well as the CMEMS Ocean State Report: de Pascual-Collar et al., 2024c.\n\n**CONTEXT**\n\nThe IBI area is a very complex region characterized by a remarkable variety of ocean currents. Among them, Podemos destacar las que se originan como resultado del closure of the North Atlantic Drift (Mason et al., 2011; Holliday et al., 2008; Peliz et al., 2007; Bower et al., 2002; Knoll et al., 2002; P\u00e9rez et al., 2001; Jia, 2000), las corrientes subsuperficiales que fluyen hacia el norte a lo largo del talud continental (de Pascual-Collar et al., 2019; Pascual et al., 2018; Machin et al., 2010; Fricourt et al., 2007; Knoll et al., 2002; Maz\u00e9 et al., 1997; White & Bowyer, 1997). Y las corrientes de intercambio que se producen en el Estrecho de Gibraltar y el Mar de Alboran (Sotillo et al., 2016; Font et al., 1998; Benzohra and Millot, 1995; Tintor\u00e9 et al., 1988).\nThe variability of ocean currents in the IBI domain is relevant to the global thermohaline circulation and other climatic and environmental issues. For example, as discussed by Fasullo and Trenberth (2008), subtropical gyres play a crucial role in the meridional energy balance. The poleward salt transport of Mediterranean water, driven by subsurface slope currents, has significant implications for salinity anomalies in the Rockall Trough and the Nordic Seas, as studied by Holliday (2003), Holliday et al. (2008), and Bozec et al. (2011). The Algerian current serves as the sole pathway for Atlantic Water to reach the Western Mediterranean.\n\n**CMEMS KEY FINDINGS**\n\nThe volume transport time series show periods in which the different monitored currents exhibited significantly high or low variability. In this regard, we can mention the periods 1997-1998 and 2014-2015 for the RTE current, the period 2012-2014 in the N48 section, the years 2006 and 2017 for the ALG current, the year 2021 for the AC current, and the period 2009-2012 for the CC current.\nAdditionally, periods are detected where the anomalies are large enough (in absolute value) to indicate a reversal of the net transport of the current. This is the case for the years 1999, 2003, and 2012-2014 in the N48 section (with a net transport towards the north), the year 2017 in the ALC current (with net transport towards the west), and the year 2010 in the CC current (with net transport towards the north).\nThe trend analysis of the monitored currents does not detect any significant trends over the analyzed period (1993-2022). However, the confidence interval for the trend in the RTE section is on the verge of rejecting the hypothesis of no trend.\n\n**Figure caption**\n\nAnnual anomalies of cross-section volume transport in monitoring sections RTE, N48, AC, ALC, and CC. Time series computed and averaged from different Copernicus Marine products for each window (see section Definition) providing a multi-product result. The blue line represents the ensemble mean, and shaded grey areas represent the standard deviation of the ensemble. Red dashed lines depict the velocity value at which the direction of the current reverses. This aligns with the average transport value (with sign reversed) and the point where absolute transport becomes zero. The analysis of trends (at 95% confidence interval) computed in the period 1993\u20132021 is included (bottom right box). Trend lines (gray dashed line) are only included in the figures when a significant trend is obtained.\n\n**DOI (product):**\nhttps://doi.org/10.48670/mds-00351\n\n**References:**\n\n* Benzohra, M., Millot, C.: Characteristics and circulation of the surface and intermediate water masses off Algeria. Deep Sea Research Part I: Oceanographic Research Papers, 42(10), 1803-1830, https://doi.org/10.1016/0967-0637(95)00043-6, 1995.\n* Bower, A. S., Le Cann, B., Rossby, T., Zenk, T., Gould, J., Speer, K., Richardson, P. L., Prater, M. D., Zhang, H.-M.: Directly measured mid-depth circulation in the northeastern North Atlantic Ocean: Nature, 419, 6907, 603\u2013607, https://doi.org/10.1038/nature01078, 2002.\n* Bozec, A., Lozier, M. S., Chasignet, E. P., Halliwel, G. R.: On the variability of the Mediterranean Outflow Water in the North Atlantic from 1948 to 2006, J. Geophys. Res.-Oceans, 116, C09033, https://doi.org/10.1029/2011JC007191, 2011.\n* Fasullo, J. T., Trenberth, K. E.: The annual cycle of the energy budget. Part II: Meridional structures and poleward transports. Journal of Climate, 21(10), 2313-2325, https://doi.org/10.1175/2007JCLI1936.1, 2008.\n* Font, J., Millot, C., Salas, J., Juli\u00e1, A., Chic, O.: The drift of Modified Atlantic Water from the Alboran Sea to the eastern Mediterranean, Scientia Marina, 62-3, https://doi.org/10.3989/scimar.1998.62n3211, 1998.\n* Friocourt Y, Levier B, Speich S, Blanke B, Drijfhout SS. A regional numerical ocean model of the circulation in the Bay of Biscay, J. Geophys. Res.,112:C09008, https://doi.org/10.1029/2006JC003935, 2007.\n* Font, J., Millot, C., Salas, J., Juli\u00e1, A., Chic, O.: The drift of Modified Atlantic Water from the Alboran Sea to the eastern Mediterranean, Scientia Marina, 62-3, https://doi.org/10.3989/scimar.1998.62n3211, 1998.\n* Holliday, N. P., Hughes, S. L., Bacon, S., Beszczynska-M\u00f6ller, A., Hansen, B., Lav\u00edn, A., Loeng, H., Mork, K. A., \u00d8sterhus, S., Sherwin, T., Walczowski, W.: Reversal of the 1960s to 1990s freshening trend in the northeast North Atlantic and Nordic Seas, Geophys. Res. Lett., 35, L03614, https://doi.org/10.1029/2007GL032675, 2008.\n* Holliday, N. P.: Air\u2010sea interactionand circulation changes in the north- east Atlantic, J. Geophys. Res., 108(C8), 3259, https://doi.org/10.1029/2002JC001344, 2003.\n* Jia, Y.: Formation of an Azores Current Due to Mediterranean Overflow in a Modeling Study of the North Atlantic. J. Phys. Oceanogr., 30, 9, 2342\u20132358, https://doi.org/10.1175/1520-0485(2000)030<2342:FOAACD>2.0.CO;2, 2000.\n* Knoll, M., Hern\u00e1ndez-Guerra, A., Lenz, B., L\u00f3pez Laatzen, F., Mach\u0131\u0301n, F., M\u00fcller, T. J., Siedler, G.: The Eastern Boundary Current system between the Canary Islands and the African Coast, Deep-Sea Research. 49-17, 3427-3440, https://doi.org/10.1016/S0967-0645(02)00105-4, 2002.\n* Lozier, M. S., Stewart, N. M.: On the temporally varying penetration ofMediterranean overflowwaters and eastward penetration ofLabrador Sea Water, J. Phys. Oceanogr., 38,2097\u20132103, https://doi.org/10.1175/2008JPO3908.1, 2008.\n* Mach\u00edn, F., Pelegr\u00ed, J. L., Fraile-Nuez, E., V\u00e9lez-Belch\u00ed, P., L\u00f3pez-Laatzen, F., Hern\u00e1ndez-Guerra, A., Seasonal Flow Reversals of Intermediate Waters in the Canary Current System East of the Canary Islands. J. Phys. Oceanogr, 40, 1902\u20131909, https://doi.org/10.1175/2010JPO4320.1, 2010.\n* Mason, E., Colas, F., Molemaker, J., Shchepetkin, A. F., Troupin, C., McWilliams, J. C., Sangra, P.: Seasonal variability of the Canary Current: A numerical study. Journal of Geophysical Research: Oceans, 116(C6), https://doi.org/10.1029/2010JC006665, 2011.\n* Maz\u00e9, J. P., Arhan, M., Mercier, H., Volume budget of the eastern boundary layer off the Iberian Peninsula, Deep-Sea Research. 1997, 44(9-10), 1543-1574, https://doi.org/10.1016/S0967-0637(97)00038-1, 1997. Maz\u00e9, J. P., Arhan, M., Mercier, H., Volume budget of the eastern boundary layer off the Iberian Peninsula, Deep-Sea Research. 1997, 44(9-10), 1543-1574, https://doi.org/10.1016/S0967-0637(97)00038-1, 1997.\n* Pascual, A., Levier ,B., Sotillo, M., Verbrugge, N., Aznar, R., Le Cann, B.: Characterization of Mediterranean Outflow Water in the Iberia-Gulf of Biscay-Ireland region. In: von Schuckmann et al. (2018) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, https://doi.org/10.1080/1755876X.2018.1489208, 2018.\n* de Pascual-Collar, A., Aznar, R., Levirer, B., Sotillo, M.: EU Copernicus Marine Service Product Quality Information Document for Global Reanalysis Products, OMI_CURRENTS_VOLTRANS_section_integrated_anomalies, Issue 1.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-IBI-OMI-QUID-CIRCULATION-VOLTRANS_section_integrated_anomalies.pdf, 2024a.\n* de Pascual-Collar, A., Aznar, R., Levirer, B., Sotillo, M.: EU Copernicus Marine Service Product User Manual for OMI_CURRENTS_VOLTRANS_section_integrated_anomalies. Issue 1.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-IBI-OMI-PUM-CIRCULATION-VOLTRANS_section_integrated_anomalies.pdf, 2024b.\n* de Pascual-Collar, A., Aznar, R., Levier, B., Garc\u00eda-Sotillo, M.: Monitoring Main Ocean Currents of the IBI Region, in: 8th edition of the Copernicus Ocean State Report (OSR8), accepted pending of publication, 2004c.\n* de Pascual-Collar, A., Sotillo, M. G., Levier, B., Aznar, R., Lorente, P., Amo-Baladr\u00f3n, A., \u00c1lvarez-Fanjul E.: Regional circulation patterns of Mediterranean Outflow Water near the Iberian and African continental slopes. Ocean Sci., 15, 565\u2013582. https://doi.org/10.5194/os-15-565-2019, 2019.\n* Peliz, A., Dubert, J., Marchesiello, P., Teles\u2010Machado, A.: Surface circulation in the Gulf of Cadiz: Model and mean flow structure. Journal of Geophysical Research: Oceans, 112, C11, https://doi.org/10.1029/2007JC004159, 2007.\n* Perez, F. F., Castro, C. G., \u00c1lvarez-Salgado, X. A., R\u00edos, A. F.: Coupling between the Iberian basin-scale circulation and the Portugal boundary current system: a chemical study, Deep-Sea Research. I 48,1519 -1533, https://doi.org/10.1016/S0967-0637(00)00101-1, 2001.\n* Sotillo, M. G., Amo-Baladr\u00f3n, A., Padorno, E., Garcia-Ladona, E., Orfila, A., Rodr\u00edguez-Rubio, P., Conti, D., Jim\u00e9nez Madrid, J. A., de los Santos, F. J., Alvarez Fanjul E.: How is the surface Atlantic water inflow through the Gibraltar Strait forecasted? A lagrangian validation of operational oceanographic services in the Alboran Sea and the Western Mediterranean, Deep-Sea Research. 133, 100-117, https://doi.org/10.1016/j.dsr2.2016.05.020, 2016.\n* Tintore, J., La Violette, P. E., Blade, I., Cruzado, A.: A study of an intense density front in the eastern Alboran Sea: the Almeria\u2013Oran front. Journal of Physical Oceanography, 18, 10, 1384-1397, https://doi.org/10.1175/1520-0485(1988)018%3C1384:ASOAID%3E2.0.CO;2, 1988.\n* White, M., Bowyer, P.: The shelf-edge current north-west of Ireland. Annales Geophysicae 15, 1076\u20131083. https://doi.org/10.1007/s00585-997-1076-0, 1997.\n", "doi": "10.48670/mds-00351", "instrument": null, "keywords": "coastal-marine-environment,cur-armor,cur-glo-myp,cur-ibi-myp,cur-mean,cur-med-myp,cur-nws-myp,cur-std,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-circulation-voltrans-ibi-section-integrated-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Volume Transport Anomaly in Selected Vertical Sections"}, "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe Ocean Freshwater Content (OFC) is calculated according to Boyer et al. (2007)\nOFC = \u03c1(Sref, Tref, p) / \u03c1(0, Tref, p ) \u00b7 ( Sref - S) / Sref\nwhere S(x, y, z, t) and Sref (x, y, z) are actual salinity and reference salinity, respectively, and x,y,z,t are zonal, meridional, vertical and temporal coordinates, respectively. The density, \u03c1, is calculated according to the TEOS10 (IOC et al., 2010). The key issue of OFC calculations lies in how the reference salinity is defined. The climatological range of salinity in the Baltic Sea varies from the freshwater conditions in the northern and eastern parts to the oceanic water conditions in the Kattegat. We follow the Boyer et al. (2007) formulation and calculate the climatological OFC from the three-dimensional temperature (Tref) and salinity (Sref) fields averaged over the period of 1993\u20132014.\nThe method for calculating the ocean freshwater content anomaly is based on the daily mean sea water salinity fields (S) derived from the Baltic Sea reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The total freshwater content anomaly is determined using the following formula:\nOFC(t) = \u222dV OFC(x, y, z, t) dx dy dz\nThe vertical integral is computed using the static cell vertical thicknesses (dz) sourced from the reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011 dataset cmems_mod_bal_phy_my_static, spanning from the sea surface to the 300 m depth. Spatial integration is performed over the Baltic Sea spatial domain, defined as the region between 9\u00b0 - 31\u00b0 E and 53\u00b0 - 66\u00b0 N using product grid definition in cmems_mod_bal_phy_my_static. \nWe evaluate the uncertainty from the mean standard deviation of monthly mean OFC. The shaded area in the figure corresponds to the annual standard deviation of monthly mean OFC. \nLinear trend (km3y-1) has been estimated from the annual anomalies with the uncertainty of 1.96-times standard error.\n\n**CONTEXT**\nClimate warming has resulted in the intensification of the global hydrological cycle but not necessarily on the regional scale (Pratap and Markonis, 2022). The increase of net precipitation over land and sea areas, decrease of ice cover, and increase of river runoff are the main components of the global hydrological cycle that increase freshwater content in the ocean (Boyer et al., 2007) and decrease ocean salinity.\nThe Baltic Sea is one of the marginal seas where water salinity and OFC are strongly influenced by the water exchange with the North Sea. The Major Baltic Inflows (MBIs) are the most voluminous event-type sources of saline water to the Baltic Sea (Mohrholz, 2018). The frequency and intensity of the MBIs and other large volume inflows have no long-term trends but do have a multidecadal variability of about 30 years (Mohrholz, 2018; Lehmann and Post, 2015; Lehmann et al., 2017; Radtke et al., 2020). Smaller barotropic and baroclinically driven inflows transport saline water into the halocline or below it, depending on the density of the inflow water (Reissmann et al., 2009). \n\n**KEY FINDINGS**\n\nThe Baltic Sea's ocean freshwater content is exhibiting a declining trend of -37\u00b18.8 km\u00b3/year, along with decadal fluctuations as also noted by Lehmann et al. (2022). Elevated freshwater levels were recorded prior to the Major Baltic Inflows of 1993, 2002, and 2013, which subsequently led to a swift decrease in freshwater content. The lowest ocean freshwater content was recorded in 2019. Over the past four years, the freshwater content anomaly has remained comparatively stable. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00347\n\n**References:**\n\n* Boyer, T., Levitus, S., Antonov, J., Locarnini, R., Mishonov, A., Garcia, H., Josey, S.A., 2007. Changes in freshwater content in the North Atlantic Ocean 1955\u20132006. Geophysical Research Letters, 34(16), L16603. Doi: 10.1029/2007GL030126\n* IOC, SCOR and IAPSO, 2010: The international thermodynamic equation of seawater - 2010: Calculation and use of thermodynamic properties. Intergovernmental Oceanographic Commission, Manuals and Guides No. 56, UNESCO (English), 196 pp. Available from http://www.TEOS-10.org (11.10.2021).\n* Lehmann, A., Post, P., 2015. Variability of atmospheric circulation patterns associated with large volume changes of the Baltic Sea. Advances in Science and Research, 12, 219\u2013225, doi:10.5194/asr-12-219-2015\n* Lehmann, A., H\u00f6flich, K., Post, P., Myrberg, K., 2017. Pathways of deep cyclones associated with large volume changes (LVCs) and major Baltic inflows (MBIs). Journal of Marine Systems, 167, pp.11-18. doi:10.1016/j.jmarsys.2016.10.014\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Mohrholz, V., 2018. Major Baltic inflow statistics\u2013revised. Frontiers in Marine Science, 5, p.384. doi:10.3389/fmars.2018.00384\n* Pratap, S., Markonis, Y., 2022. The response of the hydrological cycle to temperature changes in recent and distant climatic history, Progress in Earth and Planetary Science 9(1),30. doi:10.1186/s40645-022-00489-0\n* Radtke, H., Brunnabend, S.-E., Gr\u00e4we, U., Meier, H. E. M., 2020. Investigating interdecadal salinity changes in the Baltic Sea in a 1850\u20132008 hindcast simulation, Climate of the Past, 16, 1617\u20131642, doi:10.5194/cp-16-1617-2020\n* Reissmann, J. H., Burchard, H., Feistel,R., Hagen, E., Lass, H. U., Mohrholz, V., Nausch, G., Umlauf, L., Wiecczorek, G., 2009. Vertical mixing in the Baltic Sea and consequences for eutrophication a review, Progress in Oceanography, 82, 47\u201380. doi:10.1016/j.pocean.2007.10.004\n", "doi": "10.48670/mds-00347", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,ofc-balrean,ofc-balrean-lower-rmsd,ofc-balrean-upper-rmsd,omi-climate-ofc-baltic-area-averaged-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ocean Freshwater Content Anomaly (0-300m) from Reanalysis"}, "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 300 m depth:\nOHC=\u222b_(z_1)^(z_2)\u03c1_0 c_p (T_m-T_clim )dz [1]\nwith a reference density = 1020 kg m-3 and a specific heat capacity of cp = 3980 J kg-1 \u00b0C-1 (e.g. von Schuckmann et al., 2009; Lima et al., 2020); T_m corresponds to the monthly average temperature and T_clim is the climatological temperature of the corresponding month that varies according to each individual product.\nTime series of monthly mean values area averaged ocean heat content is provided for the Black Sea (40.86\u00b0N, 46.8\u00b0N; 27.32\u00b0E, 41.96\u00b0E) and is evaluated in areas where the topography is deeper than 300m. The Azov and Marmara Seas are not considered.\nThe quality evaluation of OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies is based on the \u201cmulti-product\u201d approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean\u2019s experience (Masina et al., 2017). Three global products and one regional (Black Sea) product have been used to build an ensemble mean, and its associated ensemble spread. Details on the products are delivered in the PUM and QUID of this OMI.\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future.\nSeveral studies discuss a warming in the Black Sea using either observations or model results (Akpinar et al., 2017; Stanev et al. 2019; Lima et al. 2020). Using satellite sea surface temperature observations (SST), Degtyarev (2000) detected a positive temperature trend of 0.016 \u00baC years-1 in the 50-100 m layer from 1985 to 1997. From Argo floats Stanev et al. (2019) found a warming trend in the cold intermediate layer (CIL; at approximately 25 \u2013 70 m) of about 0.05 oC year-1 in recent years. The warming signal was also present in ocean heat content analyses conducted by Lima et al. (2020). Their results from the Black Sea regional reanalysis showed an increase rate of 0.880\u00b10.181 W m-2 in the upper layers (0 \u2013 200 m), which has been reflected in the disappearance of Black Sea cold intermediate layer in recent years. The newest version of reanalysis also presents a warming of 0.814\u00b10.045 W m-2 in 0 \u2013 200 m (Lima et al. (2021). This warming has been reflected in a more incidence of marine heat waves in the Black Sea over the past few years (Mohammed et al. 2022).\n\n**CMEMS KEY FINDINGS**\n\nTime series of ocean heat content anomalies present a significant interannual variability, altering between cool and warm events. This important characteristic becomes evident over the years 2012 to 2015: a minimum of ocean heat content anomaly is registered close to \u2013 2.00 x 108 J m-2 in 2012, followed by positive values around 2.00 x 108 J m-2 in 2013 and above 2.0 x 108 J m-2 most of time in 2014 and 2015. Since 2005 the Black Sea experienced an increase in ocean heat content (0-300 m), and record OHC values are noticed in 2020. The Black Sea is warming at a rate of 0.995\u00b10.084 W m-2, which is higher than the global average warming rate.\nThe increase in ocean heat content weakens the CIL, whereas its decreasing favours the CIL restoration (Akpinar et al., 2017). The years 2012 and 2017 exhibited a more evident warming interruption that induced a replenishment of the CIL (Lima et al. 2021).\n\n**Figure caption**\n\nTime series of the ensemble mean and ensemble spread (shaded area) of the monthly Black Sea averaged ocean heat content anomalies integrated over the 0-300m depth layer (J m\u20132) during Jan 2005 \u2013 December 2020. The monthly ocean heat content anomalies are defined as the deviation from the climatological ocean heat content mean (1993\u20132014) of each corresponding month. Mean trend values are also reported at the bottom right corner. The ensemble is based on different data products, i.e. Black Sea Reanalysis, global ocean reanalysis GLORYS12V1; global observational based products CORA5.2, ARMOR3D. Details on the products are given in the corresponding PUM and QUID for this OMI.\n\n**DOI (product):** \n\u00a0https://doi.org/10.48670/moi-00306\n\n**References:**\n\n* Akpinar, A., Fach, B. A., Oguz, T., 2017: Observing the subsurface thermal signature of the Black Sea cold intermediate layer with Argo profiling floats. Deep Sea Res. I Oceanogr. Res. Papers 124, 140\u2013152. doi: 10.1016/j.dsr.2017.04.002.\n* Lima, L., Peneva, E., Ciliberti, S., Masina, S., Lemieux, B., Storto, A., Chtirkova, B., 2020: Ocean heat content in the Black Sea. In: Copernicus marine service Ocean State Report, issue 4, Journal of Operational Oceanography, 13:Sup1, s41\u2013s47, doi: 10.1080/1755876X.2020.1785097.\n* Lima L., Ciliberti S. A., Aydo\u011fdu A., Masina S., Escudier R., Cipollone A., Azevedo D., Causio S., Peneva E., Lecci R., Clementi E., Jansen E., Ilicak M., Cret\u00ec S., Stefanizzi L., Palermo F., Coppini G., 2021: Climate Signals in the Black Sea From a Multidecadal Eddy-Resolving Reanalysis, Frontier in Marine Science, 8:710973, doi: 10.3389/fmars.2021.710973.\n* Masina S., A. Storto, N. Ferry, M. Valdivieso, K. Haines, M. Balmaseda, H. Zuo, M. Drevillon, L. Parent, 2017: An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Climate Dynamics, 49 (3): 813-841, DOI: 10.1007/s00382-015-2728-5.\n* Stanev, E. V., Peneva, E., and Chtirkova, B. 2019: Climate change and regional ocean water mass disappearance: case of the Black Sea. J. Geophys. Res. Oceans, 124, 4803\u20134819, doi: 10.1029/2019JC015076.\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann et al., 2018: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:Sup1, s1-s142, doi: 10.1080/1755876X.2018.1489208.\n* Degtyarev, A. K., 2000: Estimation of temperature increase of the Black Sea active layer during the period 1985\u2013 1997, Meteorilogiya i Gidrologiya, 6, 72\u2013 76 (in Russian).\n", "doi": "10.48670/moi-00306", "instrument": null, "keywords": "black-sea,coastal-marine-environment,in-situ-observation,integral-wrt-depth-of-sea-water-temperature-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-ohc-blksea-area-averaged-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Ocean Heat Content Anomaly (0-300m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-20210) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 2000 m depth:\n \n With a reference density of \u03c10 = 1030 kgm-3 and a specific heat capacity of cp = 3980 J/kg\u00b0C (e.g. von Schuckmann et al., 2009)\nAveraged time series for ocean heat content and their error bars are calculated for the Iberia-Biscay-Ireland region (26\u00b0N, 56\u00b0N; 19\u00b0W, 5\u00b0E).\nThis OMI is computed using IBI-MYP, GLO-MYP reanalysis and CORA, ARMOR data from observations which provide temperatures. Where the CMEMS product for each acronym is:\n\u2022\tIBI-MYP: IBI_MULTIYEAR_PHY_005_002 (Reanalysis)\n\u2022\tGLO-MYP: GLOBAL_REANALYSIS_PHY_001_031 (Reanalysis)\n\u2022\tCORA: INSITU_GLO_TS_OA_REP_OBSERVATIONS_013_002_b (Observations)\n\u2022\tARMOR: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (Reprocessed observations)\nThe figure comprises ensemble mean (blue line) and the ensemble spread (grey shaded). Details on the product are given in the corresponding PUM for this OMI as well as the CMEMS Ocean State Report: von Schuckmann et al., 2016; von Schuckmann et al., 2018.\n\n**CONTEXT**\n\nChange in OHC is a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and can impact marine ecosystems and human livelihoods (IPCC, 2019). Additionally, OHC is one of the six Global Climate Indicators recommended by the World Meterological Organisation (WMO, 2017). \nIn the last decades, the upper North Atlantic Ocean experienced a reversal of climatic trends for temperature and salinity. While the period 1990-2004 is characterized by decadal-scale ocean warming, the period 2005-2014 shows a substantial cooling and freshening. Such variations are discussed to be linked to ocean internal dynamics, and air-sea interactions (Fox-Kemper et al., 2021; Collins et al., 2019; Robson et al 2016). Together with changes linked to the connectivity between the North Atlantic Ocean and the Mediterranean Sea (Masina et al., 2022), these variations affect the temporal evolution of regional ocean heat content in the IBI region.\nRecent studies (de Pascual-Collar et al., 2023) highlight the key role that subsurface water masses play in the OHC trends in the IBI region. These studies conclude that the vertically integrated trend is the result of different trends (both positive and negative) contributing at different layers. Therefore, the lack of representativeness of the OHC trends in the surface-intermediate waters (from 0 to 1000 m) causes the trends in intermediate and deep waters (from 1000 m to 2000 m) to be masked when they are calculated by integrating the upper layers of the ocean (from surface down to 2000 m).\n\n**CMEMS KEY FINDINGS**\n\nThe ensemble mean OHC anomaly time series over the Iberia-Biscay-Ireland region are dominated by strong year-to-year variations, and an ocean warming trend of 0.41\u00b10.4 W/m2 is barely significant.\n\n**Figure caption**\n\nTime series of annual mean area averaged ocean heat content in the Iberia-Biscay-Ireland region (basin wide) and integrated over the 0-2000m depth layer during 1993-2022: ensemble mean (blue line) and ensemble spread (shaded area). The ensemble mean is based on different data products i.e., the IBI Reanalysis, global ocean reanalysis, and the global observational based products CORA, and ARMOR3D. Trend of ensemble mean (dashed line and bottom-right box) with 95% confidence interval computed in the period 1993-2022. Details on the products are given in the corresponding PUM and QUID for this OMI.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00316\n\n**References:**\n\n* Collins M., M. Sutherland, L. Bouwer, S.-M. Cheong, T. Fr\u00f6licher, H. Jacot Des Combes, M. Koll Roxy, I. Losada, K. McInnes, B. Ratter, E. Rivera-Arriaga, R.D. Susanto, D. Swingedouw, and L. Tibig, 2019: Extremes, Abrupt Changes and Managing Risk. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 589\u2013655. https://doi.org/10.1017/9781009157964.008.\n* Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. A\u00f0algeirsd\u00f3ttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sall\u00e9e, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211\u20131362, doi: 10.1017/9781009157896.011.\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Masina, S., Pinardi, N., Cipollone, A., Banerjee, D. S., Lyubartsev, V., von Schuckmann, K., Jackson, L., Escudier, R., Clementi, E., Aydogdu, A. and Iovino D., (2022). The Atlantic Meridional Overturning Circulation forcing the mean se level in the Mediterranean Sea through the Gibraltar transport. In: Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography,15:sup1, s119\u2013s126; DOI: 10.1080/1755876X.2022.2095169\n* Potter, R. A., and Lozier, M. S. 2004: On the warming and salinification of the Mediterranean outflow waters in the North Atlantic, Geophys. Res. Lett., 31, 1\u20134, doi:10.1029/2003GL018161.\n* Robson, J., Ortega, P., Sutton, R., 2016: A reversal of climatic trends in the North Atlantic since 2005. Nature Geosci 9, 513\u2013517. https://doi.org/10.1038/ngeo2727.\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/mds-00316", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,integral-wrt-depth-of-sea-water-potential-temperature-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-ohc-ibi-area-averaged-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Ocean Heat Content Anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OSC_MEDSEA_volume_mean": {"abstract": "**DEFINITION**\n\nOcean salt content (OSC) is defined and represented here as the volume average of the integral of salinity in the Mediterranean Sea from z1 = 0 m to z2 = 300 m depth:\n\u00afS=1/V \u222bV S dV\nTime series of annual mean values area averaged ocean salt content are provided for the Mediterranean Sea (30\u00b0N, 46\u00b0N; 6\u00b0W, 36\u00b0E) and are evaluated in the upper 300m excluding the shelf areas close to the coast with a depth less than 300 m. The total estimated volume is approximately 5.7e+5 km3.\n\n**CONTEXT**\n\nThe freshwater input from the land (river runoff) and atmosphere (precipitation) and inflow from the Black Sea and the Atlantic Ocean are balanced by the evaporation in the Mediterranean Sea. Evolution of the salt content may have an impact in the ocean circulation and dynamics which possibly will have implication on the entire Earth climate system. Thus monitoring changes in the salinity content is essential considering its link \u2028to changes in: the hydrological cycle, the water masses formation, the regional halosteric sea level and salt/freshwater transport, as well as for their impact on marine biodiversity.\nThe OMI_CLIMATE_OSC_MEDSEA_volume_mean is based on the \u201cmulti-product\u201d approach introduced in the seventh issue of the Ocean State Report (contribution by Aydogdu et al., 2023). Note that the estimates in Aydogdu et al. (2023) are provided monthly while here we evaluate the results per year.\nSix global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:\n\tThe Mediterranean Sea Reanalysis at 1/24\u00b0horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)\n\tFour global reanalyses at 1/4\u00b0horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031, \nGLORYS, C-GLORS, ORAS5, FOAM, DOI: https://doi.org/10.48670/moi-00024, Desportes et al., 2022)\n\tTwo observation-based products: \nCORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, DOI: https://doi.org/10.17882/46219, Szekely et al., 2022) and \nARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, DOI: https://doi.org/10.48670/moi-00052, Grenier et al., 2021). \nDetails on the products are delivered in the PUM and QUID of this OMI. \n\n**CMEMS KEY FINDINGS**\n\nThe Mediterranean Sea salt content shows a positive trend in the upper 300 m with a continuous increase over the period 1993-2019 at rate of 5.6*10-3 \u00b13.5*10-4 psu yr-1. \nThe overall ensemble mean of different products is 38.57 psu. During the early 1990s in the entire Mediterranean Sea there is a large spread in salinity with the observational based datasets showing a higher salinity, while the reanalysis products present relatively lower salinity. The maximum spread between the period 1993\u20132019 occurs in the 1990s with a value of 0.12 psu, and it decreases to as low as 0.02 psu by the end of the 2010s.\n\n**Figure caption**\n\nTime series of annual mean volume ocean salt content in the Mediterranean Sea (basin wide), integrated over the 0-300m depth layer during 1993-2019 (or longer according to data availability) including ensemble mean and ensemble spread (shaded area). The ensemble mean and associated ensemble spread are based on different data products, i.e., Mediterranean Sea Reanalysis (MED-REA), global ocean reanalysis (GLORYS, C-GLORS, ORAS5, and FOAM) and global observational based products (CORA and ARMOR3D). Details on the products are given in the corresponding PUM and QUID for this OMI.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00325\n\n**References:**\n\n* Aydogdu, A., Miraglio, P., Escudier, R., Clementi, E., Masina, S.: The dynamical role of upper layer salinity in the Mediterranean Sea, State of the Planet, accepted, 2023.\n* Desportes, C., Garric, G., R\u00e9gnier, C., Dr\u00e9villon, M., Parent, L., Drillet, Y., Masina, S., Storto, A., Mirouze, I., Cipollone, A., Zuo, H., Balmaseda, M., Peterson, D., Wood, R., Jackson, L., Mulet, S., Grenier, E., and Gounou, A.: EU Copernicus Marine Service Quality Information Document for the Global Ocean Ensemble Physics Reanalysis, GLOBAL_REANALYSIS_PHY_001_031, Issue 1.1, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-031.pdf (last access: 3 May 2023), 2022.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020).\n* Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Grenier, E., Verbrugge, N., Mulet, S., and Guinehut, S.: EU Copernicus Marine Service Quality Information Document for the Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD, MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, Issue 1.1, Mercator Ocean International, https: //catalogue.marine.copernicus.eu/documents/QUID/CMEMS-MOB-QUID-015-012.pdf (last access: 3 May 2023), 2021.\n* Szekely, T.: EU Copernicus Marine Service Quality Information Document for the Global Ocean-Delayed Mode gridded CORA \u2013 In-situ Observations objective analysis in Delayed Mode, INSITU_GLO_PHY_TS_OA_MY_013_052, issue 1.2, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-052.pdf (last access: 4 April 2023), 2022.\n", "doi": "10.48670/mds-00325", "instrument": null, "keywords": "coastal-marine-environment,in-situ-ts-profiles,integral-wrt-depth-of-sea-water-salinity-expressed-as-salt-content,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-osc-medsea-volume-mean,sea-level,water-mass-formation-rate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Ocean Salt Content (0-300m)"}, "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Baltic Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nThe trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nThe Baltic Sea is a relatively small semi-enclosed basin with shallow bathymetry. Different forcings have been discussed to trigger sea level variations in the Baltic Sea at different time scales. In addition to steric effects, decadal and longer sea level variability in the basin can be induced by sea water exchange with the North Sea, and in response to atmospheric forcing and climate variability (e.g., the North Atlantic Oscillation; Gr\u00e4we et al., 2019).\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Baltic Sea rises at a rate of 4.1 \uf0b1 0.8 mm/year with an acceleration of 0.10 \uf0b1\uf0200.07 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00202\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Gr\u00e4we, U., Klingbeil, K., Kelln, J., and Dangendorf, S.: Decomposing Mean Sea Level Rise in a Semi-Enclosed Basin, the Baltic Sea, J. Clim., 32, 3089\u20133108, https://doi.org/10.1175/JCLI-D-18-0174.1, 2019.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00202", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-baltic-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Black Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). \nIn the Black Sea, major drivers of change have been attributed to anthropogenic climate change (steric expansion), and mass changes induced by various water exchanges with the Mediterranean Sea, river discharge, and precipitation/evaporation changes (e.g. Volkov and Landerer, 2015). The sea level variation in the basin also shows an important interannual variability, with an increase observed before 1999 predominantly linked to steric effects, and comparable lower values afterward (Vigo et al., 2005).\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Black Sea rises at a rate of 1.00 \u00b1 0.80 mm/year with an acceleration of -0.47 \u00b1 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00215\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Vigo, I., Garcia, D., and Chao, B. F.: Change of sea level trend in the Mediterranean and Black seas, J. Mar. Res., 63, 1085\u20131100, https://doi.org/10.1357/002224005775247607, 2005.\n* Volkov, D. L. and Landerer, F. W.: Internal and external forcing of sea level variability in the Black Sea, Clim. Dyn., 45, 2633\u20132646, https://doi.org/10.1007/s00382-015-2498-0, 2015.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00215", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-blksea-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Northeast Atlantic Ocean and adjacent seas Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nUncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn this region, sea level variations are influenced by the North Atlantic Oscillation (NAO) (e.g. Delworth and Zeng, 2016) and the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). Hermans et al., 2020 also reported the dominant influence of wind on interannual sea level variability in a large part of this area. This region encompasses the Mediterranean, IBI, North-West shelf and Baltic regions with different sea level dynamics detailed in the regional indicators.\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Northeast Atlantic Ocean and adjacent seas area rises at a rate of 3.2 \u00b1 0.80 mm/year with an acceleration of 0.10 \u00b1 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00335\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Hermans, T. H. J., Le Bars, D., Katsman, C. A., Camargo, C. M. L., Gerkema, T., Calafat, F. M., Tinker, J., and Slangen, A. B. A.: Drivers of Interannual Sea Level Variability on the Northwestern European Shelf, J. Geophys. Res. Oceans, 125, e2020JC016325, https://doi.org/10.1029/2020JC016325, 2020.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Spada, G. and Melini, D.: SELEN4 (SELEN version 4.0): a Fortran program for solving the gravitationally and topographically self-consistent sea-level equation in glacial isostatic adjustment modeling, Geosci. Model Dev., 12, 5055\u20135075, https://doi.org/10.5194/gmd-12-5055-2019, 2019.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/mds-00335", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,oceanographic-geographical-features,omi-climate-sl-europe-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European Seas Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Global Ocean weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and global GIA correction of -0.3mm/yr (common global GIA correction, see Spada, 2017). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nThe trend uncertainty of 0.3 mm/yr is provided at 90% confidence interval using altimeter error budget (Gu\u00e9rou et al., 2022). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. \n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report (IPCC WGI, 2021), global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm/yr for the period 2006\u20132018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c).\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, global mean sea level rises at a rate of 3.4 \u00b1 0.3 mm/year. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA correction (Spada, 2017) to consider the ongoing movement of land. The observed global trend agrees with other recent estimates (Oppenheimer et al., 2019; IPCC WGI, 2021). About 30% of this rise can be attributed to ocean thermal expansion (WCRP Global Sea Level Budget Group, 2018; von Schuckmann et al., 2018), 60% is due to land ice melt from glaciers and from the Antarctic and Greenland ice sheets. The remaining 10% is attributed to changes in land water storage, such as soil moisture, surface water and groundwater. From year to year, the global mean sea level record shows significant variations related mainly to the El Ni\u00f1o Southern Oscillation (Cazenave and Cozannet, 2014).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00237\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Oppenheimer, M., Glavovic, B. C., Hinkel, J., Van de Wal, R., Magnan, A. K., Abd-Elgaward, A., Cai, R., Cifuentes Jara, M., DeConto, R. M., Ghosh, T., Hay, J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvari, Z.: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities \u2014 Special Report on the Ocean and Cryosphere in a Changing Climate: Chapter 4, 2019.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n* Gu\u00e9rou, A., Meyssignac, B., Prandi, P., Ablain, M., Ribes, A., and Bignalet-Cazalet, F.: Current observed global mean sea level rise and acceleration estimated from satellite altimetry and the associated uncertainty, EGUsphere, 1\u201343, https://doi.org/10.5194/egusphere-2022-330, 2022.\n* Cazenave, A. and Cozannet, G. L.: Sea level rise and its coastal impacts, Earths Future, 2, 15\u201334, https://doi.org/10.1002/2013EF000188, 2014.\n", "doi": "10.48670/moi-00237", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-global-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_GLOBAL_regional_trends": {"abstract": "**DEFINITION**\n\nThe sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. The product is distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). At each grid point, the trends/accelerations are estimated on the time series corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional GIA correction (GIA map of a 27 ensemble model following Spada et Melini, 2019) and adjusted from annual and semi-annual signals. Regional uncertainties on the trends estimates can be found in Prandi et al., 2021.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the IPCC 6th assessment report (IPCC WGI, 2021), global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm/yr for the period 2006\u20132018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and regional sea level change is also influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2019, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). \n\n**KEY FINDINGS**\n\nThe altimeter sea level trends over the [1993/01/01, 2023/07/06] period exhibit large-scale variations with trends up to +10 mm/yr in regions such as the western tropical Pacific Ocean. In this area, trends are mainly of thermosteric origin (Legeais et al., 2018; Meyssignac et al., 2017) in response to increased easterly winds during the last two decades associated with the decreasing Interdecadal Pacific Oscillation (IPO)/Pacific Decadal Oscillation (e.g., McGregor et al., 2012; Merrifield et al., 2012; Palanisamy et al., 2015; Rietbroek et al., 2016).\nPrandi et al. (2021) have estimated a regional altimeter sea level error budget from which they determine a regional error variance-covariance matrix and they provide uncertainties of the regional sea level trends. Over 1993-2019, the averaged local sea level trend uncertainty is around 0.83 mm/yr with local values ranging from 0.78 to 1.22 mm/yr. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00238\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nature Clim Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press., 2019.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., von Schuckmann, K., Melet, A., Storto, A., and Meyssignac, B.: Sea Level, Journal of Operational Oceanography, 11, s13\u2013s16, https://doi.org/10.1080/1755876X.2018.1489208, 2018.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Journal of Operational Oceanography, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* McGregor, S., Gupta, A. S., and England, M. H.: Constraining Wind Stress Products with Sea Surface Height Observations and Implications for Pacific Ocean Sea Level Trend Attribution, 25, 8164\u20138176, https://doi.org/10.1175/JCLI-D-12-00105.1, 2012.\n* Merrifield, M. A., Thompson, P. R., and Lander, M.: Multidecadal sea level anomalies and trends in the western tropical Pacific, 39, https://doi.org/10.1029/2012GL052032, 2012.\n* Meyssignac, B., Piecuch, C. G., Merchant, C. J., Racault, M.-F., Palanisamy, H., MacIntosh, C., Sathyendranath, S., and Brewin, R.: Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993\u20132011, in: Integrative Study of the Mean Sea Level and Its Components, edited by: Cazenave, A., Champollion, N., Paul, F., and Benveniste, J., Springer International Publishing, Cham, 191\u2013219, https://doi.org/10.1007/978-3-319-56490-6_9, 2017.\n* Palanisamy, H., Cazenave, A., Delcroix, T., and Meyssignac, B.: Spatial trend patterns in the Pacific Ocean sea level during the altimetry era: the contribution of thermocline depth change and internal climate variability, Ocean Dynamics, 65, 341\u2013356, https://doi.org/10.1007/s10236-014-0805-7, 2015.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Rietbroek, R., Brunnabend, S.-E., Kusche, J., Schr\u00f6ter, J., and Dahle, C.: Revisiting the contemporary sea-level budget on global and regional scales, 113, 1504\u20131509, https://doi.org/10.1073/pnas.1519132113, 2016.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat Commun, 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00238", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-global-regional-trends,satellite-observation,tendency-of-sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Mean Sea Level trend map from Observations Reprocessing"}, "OMI_CLIMATE_SL_IBI_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on regional mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Irish-Biscay-Iberian (IBI) Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT **\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn IBI region, the RMSL trend is modulated by decadal variations. As observed over the global ocean, the main actors of the long-term RMSL trend are associated with anthropogenic global/regional warming. Decadal variability is mainly linked to the strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) for decadal (20-30y) timescales (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019).\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the IBI area rises at a rate of 4.00 \uf0b1 0.80 mm/year with an acceleration of 0.14 \uf0b1\uf0200.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00252\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00252", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-ibi-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Iberian Biscay Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe ocean monitoring indicator of regional mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Mediterranean Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nBeside a clear long-term trend, the regional mean sea level variation in the Mediterranean Sea shows an important interannual variability, with a high trend observed between 1993 and 1999 (nearly 8.4 mm/y) and relatively lower values afterward (nearly 2.4 mm/y between 2000 and 2022). This variability is associated with a variation of the different forcing. Steric effect has been the most important forcing before 1999 (Fenoglio-Marc, 2002; Vigo et al., 2005). Important change of the deep-water formation site also occurred in the 90\u2019s. Their influence contributed to change the temperature and salinity property of the intermediate and deep water masses. These changes in the water masses and distribution is also associated with sea surface circulation changes, as the one observed in the Ionian Sea in 1997-1998 (e.g. Ga\u010di\u0107 et al., 2011), under the influence of the North Atlantic Oscillation (NAO) and negative Atlantic Multidecadal Oscillation (AMO) phases (Incarbona et al., 2016). These circulation changes may also impact the sea level trend in the basin (Vigo et al., 2005). In 2010-2011, high regional mean sea level has been related to enhanced water mass exchange at Gibraltar, under the influence of wind forcing during the negative phase of NAO (Landerer and Volkov, 2013).The relatively high contribution of both sterodynamic (due to steric and circulation changes) and gravitational, rotational, and deformation (due to mass and water storage changes) after 2000 compared to the [1960, 1989] period is also underlined by (Calafat et al., 2022).\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Mediterranean Sea rises at a rate of 2.5 \u00b1 0.8 mm/year with an acceleration of 0.01 \u00b1 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00264\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Fenoglio-Marc, L.: Long-term sea level change in the Mediterranean Sea from multi-satellite altimetry and tide gauges, Phys. Chem. Earth Parts ABC, 27, 1419\u20131431, https://doi.org/10.1016/S1474-7065(02)00084-0, 2002.\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Fenoglio-Marc, L.: Long-term sea level change in the Mediterranean Sea from multi-satellite altimetry and tide gauges, Phys. Chem. Earth Parts ABC, 27, 1419\u20131431, https://doi.org/10.1016/S1474-7065(02)00084-0, 2002.\n* Ga\u010di\u0107, M., Civitarese, G., Eusebi Borzelli, G. L., Kova\u010devi\u0107, V., Poulain, P.-M., Theocharis, A., Menna, M., Catucci, A., and Zarokanellos, N.: On the relationship between the decadal oscillations of the northern Ionian Sea and the salinity distributions in the eastern Mediterranean, J. Geophys. Res. Oceans, 116, https://doi.org/10.1029/2011JC007280, 2011.\n* Incarbona, A., Martrat, B., Mortyn, P. G., Sprovieri, M., Ziveri, P., Gogou, A., Jord\u00e0, G., Xoplaki, E., Luterbacher, J., Langone, L., Marino, G., Rodr\u00edguez-Sanz, L., Triantaphyllou, M., Di Stefano, E., Grimalt, J. O., Tranchida, G., Sprovieri, R., and Mazzola, S.: Mediterranean circulation perturbations over the last five centuries: Relevance to past Eastern Mediterranean Transient-type events, Sci. Rep., 6, 29623, https://doi.org/10.1038/srep29623, 2016.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Landerer, F. W. and Volkov, D. L.: The anatomy of recent large sea level fluctuations in the Mediterranean Sea, Geophys. Res. Lett., 40, 553\u2013557, https://doi.org/10.1002/grl.50140, 2013.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Vigo, I., Garcia, D., and Chao, B. F.: Change of sea level trend in the Mediterranean and Black seas, J. Mar. Res., 63, 1085\u20131100, https://doi.org/10.1357/002224005775247607, 2005.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n* Calafat, F. M., Frederikse, T., and Horsburgh, K.: The Sources of Sea-Level Changes in the Mediterranean Sea Since 1960, J. Geophys. Res. Oceans, 127, e2022JC019061, https://doi.org/10.1029/2022JC019061, 2022.\n", "doi": "10.48670/moi-00264", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-medsea-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the North-West Shelf Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn this region, the time series shows decadal variations. As observed over the global ocean, the main actors of the long-term sea level trend are associated with anthropogenic global/regional warming (IPCC WGII, 2021). Decadal variability is mainly linked to the Strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) for decadal (20-30y) timescales (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019). Hermans et al., 2020 also reported the dominant influence of wind on interannual sea level variability in a large part of this area. They also underscored the influence of the inverse barometer forcing in some coastal regions.\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the NWS area rises at a rate of 3.2 \uf0b1 0.8 mm/year with an acceleration of 0.09 \uf0b1\uf0200.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**Figure caption**\n\nRegional mean sea level daily evolution (in cm) over the [1993/01/01, 2022/08/04] period, from the satellite altimeter observations estimated in the North-West Shelf region, derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) altimeter sea level gridded products distributed by the Copernicus Climate Change Service (C3S), and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The annual and semi-annual periodic signals are removed, the timeseries is low-pass filtered (175 days cut-off), and the curve is corrected for the GIA using the ICE5G-VM2 GIA model (Peltier, 2004).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00271\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Hermans, T. H. J., Le Bars, D., Katsman, C. A., Camargo, C. M. L., Gerkema, T., Calafat, F. M., Tinker, J., and Slangen, A. B. A.: Drivers of Interannual Sea Level Variability on the Northwestern European Shelf, J. Geophys. Res. Oceans, 125, e2020JC016325, https://doi.org/10.1029/2020JC016325, 2020.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00271", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-northwestshelf-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Atlantic Shelf Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_BAL_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe OMI_CLIMATE_SST_BAL_area_averaged_anomalies product includes time series of monthly mean SST anomalies over the period 1982-2023, relative to the 1991-2020 climatology, averaged for the Baltic Sea. The SST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude.\nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the OMI product. \n\n**CONTEXT**\n\nSea Surface Temperature (SST) is an Essential Climate Variable (GCOS) that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change (GCOS 2010). The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (H\u00f8yer and She 2007; H\u00f8yer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, H\u00f8yer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2 oC from 1982 to 2012 considering all months of the year and 3-5 oC when only July-September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). \n\n**CMEMS KEY FINDINGS**\n\nThe basin-average trend of SST anomalies for Baltic Sea region amounts to 0.038\u00b10.004\u00b0C/year over the period 1982-2023 which corresponds to an average warming of 1.60\u00b0C. Adding the North Sea area, the average trend amounts to 0.029\u00b10.002\u00b0C/year over the same period, which corresponds to an average warming of 1.22\u00b0C for the entire region since 1982. \n\n**Figure caption**\n\nTime series of monthly mean (turquoise line) and annual mean (blue line) of sea surface temperature anomalies for January 1982 to December 2023, relative to the 1991-2020 mean, combined for the Baltic Sea and North Sea SST (OMI_CLIMATE_SST_BAL_area_averaged_anomalies). The data are based on the multi-year Baltic Sea L4 satellite SST reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00205\n\n**References:**\n\n* BACC II Author Team 2015. Second Assessment of Climate Change for the Baltic Sea Basin. Springer Science & Business Media, 501 pp., doi:10.1007/978-3-319-16006-1.\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* H\u00f8yer JL, She J. 2007. Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Mar. Syst., 65, 176\u2013189, doi:10.1016/j.jmarsys.2005.03.008.\n* Lehmann A, Getzlaff K, Harla\u00df J. 2011. Detailed assessment of climate variability of the Baltic Sea area for the period 1958\u20132009. Climate Res., 46, 185\u2013196, doi:10.3354/cr00876.\n* Karina von Schuckmann ((Editor)), Pierre-Yves Le Traon ((Editor)), Neville Smith ((Editor)), Ananda Pascual ((Editor)), Pierre Brasseur ((Editor)), Katja Fennel ((Editor)), Samy Djavidnia ((Editor)), Signe Aaboe, Enrique Alvarez Fanjul, Emmanuelle Autret, Lars Axell, Roland Aznar, Mario Benincasa, Abderahim Bentamy, Fredrik Boberg, Romain Bourdall\u00e9-Badie, Bruno Buongiorno Nardelli, Vittorio E. Brando, Cl\u00e9ment Bricaud, Lars-Anders Breivik, Robert J.W. Brewin, Arthur Capet, Adrien Ceschin, Stefania Ciliberti, Gianpiero Cossarini, Mar-ta de Alfonso, Alvaro de Pascual Collar, Jos de Kloe, Julie Deshayes, Charles Desportes, Marie Dr\u00e9villon, Yann Drillet, Riccardo Droghei, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Claudia Fratianni, Jes\u00fas Garc\u00eda La-fuente, Marcos Garcia Sotillo, Gilles Garric, Florent Gasparin, Riccardo Gerin, Simon Good, J\u00e9rome Gourrion, Marilaure Gr\u00e9goire, Eric Greiner, St\u00e9phanie Guinehut, Elodie Gutknecht, Fabrice Hernandez, Olga Hernandez, Jacob H\u00f8yer, Laura Jackson, Simon Jandt, Simon Josey, M\u00e9lanie Juza, John Kennedy, Zoi Kokkini, Gerasimos Korres, Mariliis K\u00f5uts, Priidik Lagemaa, Thomas Lavergne, Bernard le Cann, Jean-Fran\u00e7ois Legeais, Benedicte Lemieux-Dudon, Bruno Levier, Vidar Lien, Ilja Maljutenko, Fernando Manzano, Marta Marcos, Veselka Mari-nova, Simona Masina, Elena Mauri, Michael Mayer, Angelique Melet, Fr\u00e9d\u00e9ric M\u00e9lin, Benoit Meyssignac, Maeva Monier, Malte M\u00fcller, Sandrine Mulet, Cristina Naranjo, Giulio Notarstefano, Aur\u00e9lien Paulmier, Bego\u00f1a P\u00e9rez Gomez, Irene P\u00e9rez Gonzalez, Elisaveta Peneva, Coralie Perruche, K. Andrew Peterson, Nadia Pinardi, Andrea Pisano, Silvia Pardo, Pierre-Marie Poulain, Roshin P. Raj, Urmas Raudsepp, Michaelis Ravdas, Rebecca Reid, Marie-H\u00e9l\u00e8ne Rio, Stefano Salon, Annette Samuelsen, Michela Sammartino, Simone Sammartino, Anne Britt Sand\u00f8, Rosalia Santoleri, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Ad Stoffelen, Andrea Storto, Tanguy Szerkely, Susanne Tamm, Steffen Tietsche, Jonathan Tinker, Joaqu\u00edn Tintore, Ana Trindade, Daphne van Zanten, Luc Vandenbulcke, Anton Verhoef, Nathalie Verbrugge, Lena Viktorsson, Karina von Schuckmann, Sarah L. Wakelin, Anna Zacharioudaki & Hao Zuo (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n* Karina von Schuckmann, Pierre-Yves Le Traon, Enrique Alvarez-Fanjul, Lars Axell, Magdalena Balmaseda, Lars-Anders Breivik, Robert J. W. Brewin, Clement Bricaud, Marie Drevillon, Yann Drillet, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Marcos Garc\u00eda Sotillo, Gilles Garric, Florent Gasparin, Elodie Gutknecht, St\u00e9phanie Guinehut, Fabrice Hernandez, Melanie Juza, Bengt Karlson, Gerasimos Korres, Jean-Fran\u00e7ois Legeais, Bruno Levier, Vidar S. Lien, Rosemary Morrow, Giulio Notarstefano, Laurent Parent, \u00c1lvaro Pascual, Bego\u00f1a P\u00e9rez-G\u00f3mez, Coralie Perruche, Nadia Pinardi, Andrea Pisano, Pierre-Marie Poulain, Isabelle M. Pujol, Roshin P. Raj, Urmas Raudsepp, Herv\u00e9 Roquet, Annette Samuelsen, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Jonathan Tinker, Joaqu\u00edn Tintor\u00e9, Lena Viktorsson, Michael Ablain, Elin Almroth-Rosell, Antonio Bonaduce, Emanuela Clementi, Gianpiero Cossarini, Quentin Dagneaux, Charles Desportes, Stephen Dye, Claudia Fratianni, Simon Good, Eric Greiner, Jerome Gourrion, Mathieu Hamon, Jason Holt, Pat Hyder, John Kennedy, Fernando Manzano-Mu\u00f1oz, Ang\u00e9lique Melet, Benoit Meyssignac, Sandrine Mulet, Bruno Buongiorno Nardelli, Enda O\u2019Dea, Einar Olason, Aur\u00e9lien Paulmier, Irene P\u00e9rez-Gonz\u00e1lez, Rebecca Reid, Ma-rie-Fanny Racault, Dionysios E. Raitsos, Antonio Ramos, Peter Sykes, Tanguy Szekely & Nathalie Verbrugge (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n", "doi": "10.48670/moi-00205", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-bal-area-averaged-anomalies,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Surface Temperature anomaly time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_BAL_trend": {"abstract": "**DEFINITION**\n\nThe OMI_CLIMATE_SST_BAL_trend product includes the cumulative/net trend in sea surface temperature anomalies for the Baltic Sea from 1982-2023. The cumulative trend is the rate of change (\u00b0C/year) scaled by the number of years (42 years). The SST Level 4 analysis products that provide the input to the trend calculations are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude.\nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016. See the Copernicus Marine Service Ocean State Reports for more information on the OMI product (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). The times series of monthly anomalies have been used to calculate the trend in SST using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\n**CONTEXT**\n\nSST is an essential climate variable that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (H\u00f8yer and She 2007; H\u00f8yer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, H\u00f8yer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2oC from 1982 to 2012 considering all months of the year and 3-5oC when only July- September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). \n\n**CMEMS KEY FINDINGS**\n\nSST trends were calculated for the Baltic Sea area and the whole region including the North Sea, over the period January 1982 to December 2023. The average trend for the Baltic Sea domain (east of 9\u00b0E longitude) is 0.038\u00b0C/year, which represents an average warming of 1.60\u00b0C for the 1982-2023 period considered here. When the North Sea domain is included, the trend decreases to 0.029\u00b0C/year corresponding to an average warming of 1.22\u00b0C for the 1982-2023 period. \n**Figure caption**\n\n**Figure caption**\n\nCumulative trends in sea surface temperature anomalies calculated from 1982 to 2023 for the Baltic Sea (OMI_CLIMATE_SST_BAL_trend). Trend calculations are based on the multi-year Baltic Sea L4 SST satellite product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00206\n\n**References:**\n\n* BACC II Author Team 2015. Second Assessment of Climate Change for the Baltic Sea Basin. Springer Science & Business Media, 501 pp., doi:10.1007/978-3-319-16006-1.\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n* H\u00f8yer JL, She J. 2007. Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Mar. Syst., 65, 176\u2013189, doi:10.1016/j.jmarsys.2005.03.008.\n* Lehmann A, Getzlaff K, Harla\u00df J. 2011. Detailed assessment of climate variability of the Baltic Sea area for the period 1958\u20132009. Climate Res., 46, 185\u2013196, doi:10.3354/cr00876.\n* Karina von Schuckmann ((Editor)), Pierre-Yves Le Traon ((Editor)), Neville Smith ((Editor)), Ananda Pascual ((Editor)), Pierre Brasseur ((Editor)), Katja Fennel ((Editor)), Samy Djavidnia ((Editor)), Signe Aaboe, Enrique Alvarez Fanjul, Emmanuelle Autret, Lars Axell, Roland Aznar, Mario Benincasa, Abderahim Bentamy, Fredrik Boberg, Romain Bourdall\u00e9-Badie, Bruno Buongiorno Nardelli, Vittorio E. Brando, Cl\u00e9ment Bricaud, Lars-Anders Breivik, Robert J.W. Brewin, Arthur Capet, Adrien Ceschin, Stefania Ciliberti, Gianpiero Cossarini, Marta de Alfonso, Alvaro de Pascual Collar, Jos de Kloe, Julie Deshayes, Charles Desportes, Marie Dr\u00e9villon, Yann Drillet, Riccardo Droghei, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Claudia Fratianni, Jes\u00fas Garc\u00eda Lafuente, Marcos Garcia Sotillo, Gilles Garric, Florent Gasparin, Riccardo Gerin, Simon Good, J\u00e9rome Gourrion, Marilaure Gr\u00e9goire, Eric Greiner, St\u00e9phanie Guinehut, Elodie Gutknecht, Fabrice Hernandez, Olga Hernandez, Jacob H\u00f8yer, Laura Jackson, Simon Jandt, Simon Josey, M\u00e9lanie Juza, John Kennedy, Zoi Kokkini, Gerasimos Korres, Mariliis K\u00f5uts, Priidik Lagemaa, Thomas Lavergne, Bernard le Cann, Jean-Fran\u00e7ois Legeais, Benedicte Lemieux-Dudon, Bruno Levier, Vidar Lien, Ilja Maljutenko, Fernando Manzano, Marta Marcos, Veselka Marinova, Simona Masina, Elena Mauri, Michael Mayer, Angelique Melet, Fr\u00e9d\u00e9ric M\u00e9lin, Benoit Meyssignac, Maeva Monier, Malte M\u00fcller, Sandrine Mulet, Cristina Naranjo, Giulio Notarstefano, Aur\u00e9lien Paulmier, Bego\u00f1a P\u00e9rez Gomez, Irene P\u00e9rez Gonzalez, Elisaveta Peneva, Coralie Perruche, K. Andrew Peterson, Nadia Pinardi, Andrea Pisano, Silvia Pardo, Pierre-Marie Poulain, Roshin P. Raj, Urmas Raudsepp, Michaelis Ravdas, Rebecca Reid, Marie-H\u00e9l\u00e8ne Rio, Stefano Salon, Annette Samuelsen, Michela Sammartino, Simone Sammartino, Anne Britt Sand\u00f8, Rosalia Santoleri, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Ad Stoffelen, Andrea Storto, Tanguy Szerkely, Susanne Tamm, Steffen Tietsche, Jonathan Tinker, Joaqu\u00edn Tintore, Ana Trindade, Daphne van Zanten, Luc Vandenbulcke, Anton Verhoef, Nathalie Verbrugge, Lena Viktorsson, Karina von Schuckmann, Sarah L. Wakelin, Anna Zacharioudaki & Hao Zuo (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n* Karina von Schuckmann, Pierre-Yves Le Traon, Enrique Alvarez-Fanjul, Lars Axell, Magdalena Balmaseda, Lars-Anders Breivik, Robert J. W. Brewin, Clement Bricaud, Marie Drevillon, Yann Drillet, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Marcos Garc\u00eda Sotillo, Gilles Garric, Florent Gasparin, Elodie Gutknecht, St\u00e9phanie Guinehut, Fabrice Hernandez, Melanie Juza, Bengt Karlson, Gerasimos Korres, Jean-Fran\u00e7ois Legeais, Bruno Levier, Vidar S. Lien, Rosemary Morrow, Giulio Notarstefano, Laurent Parent, \u00c1lvaro Pascual, Bego\u00f1a P\u00e9rez-G\u00f3mez, Coralie Perruche, Nadia Pinardi, Andrea Pisano, Pierre-Marie Poulain, Isabelle M. Pujol, Roshin P. Raj, Urmas Raudsepp, Herv\u00e9 Roquet, Annette Samuelsen, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Jonathan Tinker, Joaqu\u00edn Tintor\u00e9, Lena Viktorsson, Michael Ablain, Elin Almroth-Rosell, Antonio Bonaduce, Emanuela Clementi, Gianpiero Cossarini, Quentin Dagneaux, Charles Desportes, Stephen Dye, Claudia Fratianni, Simon Good, Eric Greiner, Jerome Gourrion, Mathieu Hamon, Jason Holt, Pat Hyder, John Kennedy, Fernando Manzano-Mu\u00f1oz, Ang\u00e9lique Melet, Benoit Meyssignac, Sandrine Mulet, Bruno Buongiorno Nardelli, Enda O\u2019Dea, Einar Olason, Aur\u00e9lien Paulmier, Irene P\u00e9rez-Gonz\u00e1lez, Rebecca Reid, Ma-rie-Fanny Racault, Dionysios E. Raitsos, Antonio Ramos, Peter Sykes, Tanguy Szekely & Nathalie Verbrugge (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00206", "instrument": null, "keywords": "baltic-sea,change-over-time-in-sea-surface-foundation-temperature,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-bal-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "OMI_CLIMATE_SST_IBI_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe omi_climate_sst_ibi_area_averaged_anomalies product for 2022 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1993 and averaged over the Iberia-Biscay-Irish Seas. The IBI SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish Seas (SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-IBI_v2.1.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps over the European North West Shelf Iberia-Biscay-Irish Seas built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019), Copernicus Climate Change Service (C3S) initiatives and Eumetsat data. Anomalies are computed against the 1993-2014 reference period.\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018).\n\n**CMEMS KEY FINDINGS**\n\nThe overall trend in the SST anomalies in this region is 0.013 \u00b10.001 \u00b0C/year over the period 1993-2022. \n\n**Figure caption**\n\nTime series of monthly mean and 12-month filtered sea surface temperature anomalies in the Iberia-Biscay-Irish Seas during the period 1993-2022. Anomalies are relative to the climatological period 1993-2014 and built from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 satellite product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-IBI-SST.pdf). The sea surface temperature trend with its 95% confidence interval (shown in the box) is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen\u2019s method (Sen 1968).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00256\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00256", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ibi-area-averaged-anomalies,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Sea Surface Temperature time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_IBI_trend": {"abstract": "**DEFINITION**\n\nThe omi_climate_sst_ibi_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish areas over the period 1982-2023, i.e. the rate of change (\u00b0C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-IBI_v3.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish areas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. \n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\n**CMEMS KEY FINDINGS**\n\nThe overall trend in the SST anomalies in this region is 0.022 \u00b10.002 \u00b0C/year over the period 1982-2023. \n\n**Figure caption**\nSea surface temperature trend over the period 1982-2023 in the Iberia-Biscay-Irish areas. The trend is the rate of change (\u00b0C/year). The trend map in sea surface temperature is derived from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf). The trend is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen\u2019s method (Sen 1968).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00257\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389\n", "doi": "10.48670/moi-00257", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-tempsal-sst-trend,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ibi-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Sea Surface Temperature trend map from Observations Reprocessing"}, "OMI_CLIMATE_SST_IST_ARCTIC_anomaly": {"abstract": "**DEFINITION**\n\nThe OMI_CLIMATE_SST_IST_ARCTIC_anomaly product includes the 2D annual mean surface temperature anomaly for the Arctic Ocean for 2023. The annual mean surface temperature anomaly is calculated from the climatological mean estimated from 1991 to 2020, defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate,). The SST/IST Level 4 analysis that provides the input to the climatology and mean anomaly calculations are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2023. The product has a spatial resolution of 0.05 degrees in latitude and longitude. \nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly average anomalies from the reference climatology from 1991 to 2020, using the daily level 4 SST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\n**CONTEXT**\n\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al, 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\n**KEY FINDINGS**\n\nThe area average anomaly of 2023 is 1.70\u00b11.08\u00b0C (\u00b1 means 1 standard deviation in this case). The majority of anomalies are positive and exceed 2\u00b0C for most areas of the Arctic Ocean, while the largest regional anomalies exceeded 6\u00b0C. Near zero and slightly negative anomalies are observed in some areas of the Barents, Norwegian and Greenland Sea and around the Bering Strait. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00353\n\n**References:**\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42\n", "doi": "10.48670/mds-00353", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,ice-surface-temperature,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ist-arctic-anomaly,satellite-observation,sea-surface-temperature,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea and Sea Ice Surface Temperature anomaly based on reprocessed observations"}, "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies": {"abstract": "**DEFINITION **\n\nThe OMI_CLIMATE_SST_IST_ARCTIC_sst_ist_area_averaged_anomalies product includes time series of monthly mean SST/IST anomalies over the period 1982-2023, relative to the 1991-2020 climatology (30 years), averaged for the Arctic Ocean. The SST/IST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2023. The product has a spatial resolution of 0.05 degrees in latitude and longitude. \nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly average anomalies from the reference climatology from 1991 to 2020, using the daily level 4 SST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. The climatological period used is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate,). See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\n**CONTEXT**\n\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al, 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\n**KEY FINDINGS**\n\nThe basin-average trend of SST/IST anomalies for the Arctic Ocean region amounts to 0.104\u00b10.005 \u00b0C/year over the period 1982-2023 (42 years) which corresponds to an average warming of 4.37\u00b0C. The 2-d map of warming trends indicates these are highest for the Beaufort Sea, Chuckchi Sea, East Siberian Sea, Laptev Sea, Kara Sea and parts of Baffin Bay. The 2d map of Arctic anomalies for 2023 reveals regional peak warming exceeding 6\u00b0C.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00323\n\n**References:**\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n", "doi": "10.48670/mds-00323", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,ice-surface-temperature,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ist-arctic-area-averaged-anomalies,satellite-observation,sea-surface-temperature,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea and Sea Ice Surface Temperature anomaly time series based on reprocessed observations"}, "OMI_CLIMATE_SST_IST_ARCTIC_trend": {"abstract": "**DEFINITION**\n\nThe OMI_CLIMATE_sst_ist_ARCTIC_sst_ist_trend product includes the cumulative/net trend in combined sea and ice surface temperature anomalies for the Arctic Ocean from 1982-2023. The cumulative trend is the rate of change (\u00b0C/year) scaled by the number of years (42 years). The SST/IST Level 4 analysis that provides the input to the trend calculations are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2023. The product has a spatial resolution of 0.05 degrees in latitude and longitude.\n\nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly averages from the reference climatology defined over the period 1991-2020, according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate), using daily level 4 SST/IST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\n**CONTEXT**\n\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al., 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\n**KEY FINDINGS**\n\nSST/IST trends were calculated for the Arctic Ocean over the period January 1982 to December 2023. The cumulative trends are upwards of 2\u00b0C for the greatest part of the Arctic Ocean, with the largest trends occur in the Beaufort Sea, Chuckchi Sea, East Siberian Sea, Laptev Sea, Kara Sea and parts of Baffin Bay. Zero to slightly negative trends are found at the North Atlantic part of the Arctic Ocean. The combined sea and sea ice surface temperature trend is 0.104+/-0.005\u00b0C/yr, i.e. an increase by around 4.37\u00b0C between 1982 and 2023. The 2d map of Arctic anomalies reveals regional peak warming exceeding 6\u00b0C.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00324\n\n**References:**\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n", "doi": "10.48670/mds-00324", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,ice-surface-temperature,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ist-arctic-trend,satellite-observation,sea-surface-temperature,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea and Sea Ice Surface Temperature 2D trend from climatology based on reprocessed observations"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe omi_climate_sst_northwestshelf_area_averaged_anomalies product for 2023 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the European North West Shelf Seas. The NORTHWESTSHELF SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish areas(SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST- NORTHWESTSHELF_v3.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps over the European North West Shelf Iberai-Biscay-Irish Seas built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Anomalies are computed against the 1991-2020 reference period. \n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\n**CMEMS KEY FINDINGS **\n\nThe overall trend in the SST anomalies in this region is 0.024 \u00b10.002 \u00b0C/year over the period 1982-2023. \n\n**Figure caption**\n\nTime series of monthly mean and 24-month filtered sea surface temperature anomalies in the European North West Shelf Seas during the period 1982-2023. Anomalies are relative to the climatological period 1991-2020 and built from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 satellite product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-NORTHWESTSHELF-SST.pdf). The sea surface temperature trend with its 95% confidence interval (shown in the box) is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen\u2019s method (Sen 1968).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00275\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00275", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-climate-sst-northwestshelf-area-averaged-anomalies,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf Sea Surface Temperature time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_trend": {"abstract": "**DEFINITION**\n\nThe omi_climate_sst_northwestshelf_trend product includes the Sea Surface Temperature (SST) trend for the European North West Shelf Seas over the period 1982-2023, i.e. the rate of change (\u00b0C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-NORTHWESTSHELF_v3.pdf), which provided the SSTs used to compute the SST trend over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. \n\n**CONTEXT **\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\n**CMEMS KEY FINDINGS **\n\nOver the period 1982-2023, the European North West Shelf Seas mean Sea Surface Temperature (SST) increased at a rate of 0.024 \u00b1 0.002 \u00b0C/Year.\n\n**Figure caption**\n\nSea surface temperature trend over the period 1982-2023 in the European North West Shelf Seas. The trend is the rate of change (\u00b0C/year). The trend map in sea surface temperature is derived from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf). The trend is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005;) and Sen\u2019s method (Sen 1968).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00276\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00276", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-climate-sst-northwestshelf-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf Sea Surface Temperature trend map from Observations Reprocessing"}, "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection": {"abstract": "**DEFINITION**\n\nThe North Pacific Gyre Oscillation (NPGO) is a climate pattern introduced by Di Lorenzo et al. (2008) and further reported by Tranchant et al. (2019) in the CMEMS Ocean State Report #3. The NPGO is defined as the second dominant mode of variability of Sea Surface Height (SSH) anomaly and SST anomaly in the Northeast Pacific (25\u00b0\u2013 62\u00b0N, 180\u00b0\u2013 250\u00b0E). The spatial and temporal pattern of the NPGO has been deduced over the [1950-2004] period using an empirical orthogonal function (EOF) decomposition on sea level and sea surface temperature fields produced by the Regional Ocean Modeling System (ROMS) (Di Lorenzo et al., 2008; Shchepetkin and McWilliams, 2005). Afterward, the sea level spatial pattern of the NPGO is used/projected with satellite altimeter delayed-time sea level anomalies to calculate and update the NPGO index.\nThe NPGO index disseminated on CMEMS was specifically updated from 2004 onward using up-to-date altimeter products (DT2021 version; SEALEVEL_GLO_PHY_L4_MY _008_047 CMEMS product, including \u201cmy\u201d & \u201cmyint\u201d datasets, and the near-real time SEALEVEL_GLO_PHY_L4_NRT _008_046 CMEMS product). Users that previously used the index disseminated on www.o3d.org/npgo/ web page will find slight differences induced by this update. The change in the reprocessed version (previously DT-2018) and the extension of the mean value of the SSH anomaly (now 27 years, previously 20 years) induce some slight changes not impacting the general variability of the NPGO. \n\n**CONTEXT**\n\nNPGO mode emerges as the leading mode of decadal variability for surface salinity and upper ocean nutrients (Di Lorenzo et al., 2009). The North Pacific Gyre Oscillation (NPGO) term is used because its fluctuations reflect changes in the intensity of the central and eastern branches of the North Pacific gyres circulations (Chhak et al., 2009). This index measures change in the North Pacific gyres circulation and explains key physical-biological ocean variables including temperature, salinity, sea level, nutrients, chlorophyll-a. A positive North Pacific Gyre Oscillation phase is a dipole pattern with negative SSH anomaly north of 40\u00b0N and the opposite south of 40\u00b0N. (Di Lorenzo et al., 2008) suggested that the North Pacific Gyre Oscillation is the oceanic expression of the atmospheric variability of the North Pacific Oscillation (Walker and Bliss, 1932), which has an expression in both the 2nd EOFs of SSH and Sea Surface Temperature (SST) anomalies (Ceballos et al., 2009). This finding is further supported by the recent work of (Yi et al., 2018) showing consistent pattern features between the atmospheric North Pacific Oscillation and the oceanic North Pacific Gyre Oscillation in the Coupled Model Intercomparison Project Phase 5 (CMIP5) database.\n\n**CMEMS KEY FINDINGS**\n\nThe NPGO index is presently in a negative phase, associated with a positive SSH anomaly north of 40\u00b0N and negative south of 40\u00b0N. This reflects a reduced amplitude of the central and eastern branches of the North Pacific gyre, corresponding to a reduced coastal upwelling and thus a lower sea surface salinity and concentration of nutrients. \n\n**Figure caption**\n\nNorth Pacific Gyre Oscillation (NPGO) index monthly averages. The NPGO index has been projected on normalized satellite altimeter sea level anomalies. The NPGO index is derived from (Di Lorenzo et al., 2008) before 2004, the DUACS delayed-time (reprocessed version DT-2021, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) completed by DUACS near Real Time (\u201cnrt\u201d) sea level multi-mission gridded products. The vertical red lines show the date of the transition between the historical Di Lorenzo\u2019s series and the DUACS product, then between the DUACS \u201cmyint\u201d and \u201cnrt\u201d products used.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00221\n\n**References:**\n\n* Ceballos, L. I., E. Di Lorenzo, C. D. Hoyos, N. Schneider and B. Taguchi, 2009: North Pacific Gyre Oscillation Synchronizes Climate Fluctuations in the Eastern and Western Boundary Systems. Journal of Climate, 22(19) 5163-5174, doi:10.1175/2009jcli2848.1\n* Chhak, K. C., E. Di Lorenzo, N. Schneider and P. F. Cummins, 2009: Forcing of Low-Frequency Ocean Variability in the Northeast Pacific. Journal of Climate, 22(5) 1255-1276, doi:10.1175/2008jcli2639.1.\n* Di Lorenzo, E., N. Schneider, K.M. Cobb, K. Chhak, P.J.S. Franks, A.J. Miller, J.C. McWilliams, S.J. Bograd, H. Arango, E. Curchister, and others. 2008. North Pacific Gyre Oscillation links ocean climate and ecosystem change. Geophysical Research Letters 35, L08607, https://doi.org/10.1029/2007GL032838.\n* Di Lorenzo, E., J. Fiechter, N. Schneider, A. Bracco, A. J. Miller, P. J. S. Franks, S. J. Bograd, A. M. Moore, A. C. Thomas, W. Crawford, A. Pena and A. J. Hermann, 2009: Nutrient and salinity decadal variations in the central and eastern North Pacific. Geophysical Research Letters, 36, doi:10.1029/2009gl038261.\n* Di Lorenzo, E., K. M. Cobb, J. C. Furtado, N. Schneider, B. T. Anderson, A. Bracco, M. A. Alexander and D. J. Vimont, 2010: Central Pacific El Nino and decadal climate change in the North Pacific Ocean. Nature Geoscience, 3(11) 762-765, doi:10.1038/ngeo984\n* Tranchant, B. I. Pujol, E. Di Lorenzo and JF Legeais (2019). The North Pacific Gyre Oscillation. In: Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, s26\u2013s30; DOI: 10.1080/ 1755876X.2019.1633075\n* Yi, D. L., Gan, B. Wu., L., A.J. Miller, 2018. The North Pacific Gyre Oscillation and Mechanisms of Its Decadal Variability in CMIP5 Models: Journal of Climate: Vol 31, No 6, 2487-2509.\n", "doi": "10.48670/moi-00221", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-climvar-pacific-npgo-sla-eof-mode-projection,satellite-observation,tendency-of-sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Pacific Gyre Oscillation from Observations Reprocessing"}, "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nTemperature deviation from the 30-year (1991-2020) daily climatological mean temperature for the Barents Sea region (68\u00b0N - 80\u00b0N, 18\u00b0E - 55\u00b0E), relative to the difference between the daily climatological average and the 90th percentile above the climatological mean. Thus, when the index is above 1 the area is in a state of a marine heatwave, and when the index is below -1 the area is in a state of a marine cold spell, following the definition by Hobday et al. (2016). For further details, see Lien et al. (2024).\"\"\n\n**CONTEXT**\nAnomalously warm oceanic events, often termed marine heatwaves, can potentially impact the ecosystem in the affected region. The marine heatwave concept and terminology was systemized by Hobday et al. (2016), and a generally adopted definition of a marine heatwave is a period of more than five days where the temperature within a region exceeds the 90th percentile of the seasonally varying climatological average temperature for that region. The Barents Sea region has warmed considerably during the most recent climatological average period (1991-2020) due to a combination of climate warming and positive phase of regional climate variability (e.g., Lind et al., 2018 ; Skagseth et al., 2020 ; Smedsrud et al., 2022), with profound consequences for marine life where boreal species are moving northward at the expense of arctic species (e.g., Fossheim et al., 2015; Oziel et al., 2020; Husson et al., 2022).\n\n**KEY FINDINGS**\n\nThere is a clear tendency of reduced frequency and intensity of marine cold spells, and a tendency towards increased frequency and intensity of marine heat waves in the Barents Sea. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00346\n\n**References:**\n\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Clim Change. doi:10.1038/nclimate2647\n* Hobday AJ, Alexander LV, Perkins SE, Smale DA, Straub SC, Oliver ECJ, Benthuysen JA, Burrows MT, Donat MG, Feng M, Holbrook NJ, Moore PJ, Scannell HA, Gupta AS, Wernberg T. 2016. A hierarchical approach to defining marine heatwaves. Progr. Oceanogr., 141, 227-238\n* Husson B, Lind S, Fossheim M, Kato-Solvang H, Skern-Mauritzen M, P\u00e9cuchet L, Ingvaldsen RB, Dolgov AV, Primicerio R. 2022. Successive extreme climatic events lead to immediate, large-scale, and diverse responses from fish in the Arctic. Global Change Biol, 28, 3728-3744\n* Lien VS, Raj RP, Chatterjee S. 2024. Surface and bottom marine heatwave characteristics in the Barents Sea: a model study. State of the Planet (in press)\n* Lind S, Ingvaldsen RB, Furevik T. 2018. Arctic warming hotspot in the northern Barents Sea linked to declining sea-ice import. Nat Clim Change, 8, 634-639\n* Oziel L, Baudena A, Ardyna M, Massicotte P, Randelhoff A, Sallee J-B, Ingvaldsen RB, Devred E, Babin M. 2020. Faster Atlantic currents drive poleward expansion of temperate phytoplankton in the Arctic Ocean. Nat Commun., 11(1), 1705, doi:10.1038/s41467-020-15485-5\n* Skagseth \u00d8, Eldevik T, \u00c5rthun M, Asbj\u00f8rnsen H, Lien VS, Smedsrud LH. 2020. Reduced efficiency of the Barents Sea cooling machine. Nat Clim Change, doi.org/10.1038/s41558-020-0772-6\n* Smedsrud LH, Muilwijk M, Brakstad A, Madonna E, Lauvset SK, Spensberger C, Born A, Eldevik T, Drange H, Jeansson E, Li C, Olsen A, Skagseth \u00d8, Slater DA, Straneo F, V\u00e5ge K, \u00c5rthun M. 2022.\n* Nordic Seas heat loss, Atlantic inflow, and Arctic sea ice cover over the last century. Rev Geophys., 60, e2020RG000725\n", "doi": "10.48670/mds-00346", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mhw-index-bottom,mhw-index-surface,multi-year,numerical-model,oceanographic-geographical-features,omi-extreme-mhw-arctic-area-averaged-anomalies,temperature-bottom,temperature-surface,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1991-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Marine Heatwave Index in the Barents Sea from Reanalysis"}, "OMI_EXTREME_SEASTATE_GLOBAL_swh_mean_and_P95_obs": {"abstract": "**DEFINITION**\n\nSignificant wave height (SWH), expressed in metres, is the average height of the highest one-third of waves. This OMI provides time series of seasonal mean and extreme SWH values in three oceanic regions as well as their trends from 2002 to 2020, computed from the reprocessed global L4 SWH product (WAVE_GLO_PHY_SWH_L4_MY_014_007). The extreme SWH is defined as the 95th percentile of the daily maximum of SWH over the chosen period and region. The 95th percentile represents the value below which 95% of the data points fall, indicating higher wave heights than usual. The mean and the 95th percentile of SWH are calculated for two seasons of the year to take into account the seasonal variability of waves (January, February, and March, and July, August, and September) and are in m while the trends are in cm/yr.\n\n**CONTEXT**\n\nGrasping the nature of global ocean surface waves, their variability, and their long-term interannual shifts is essential for climate research and diverse oceanic and coastal applications. The sixth IPCC Assessment Report underscores the significant role waves play in extreme sea level events (Mentaschi et al., 2017), flooding (Storlazzi et al., 2018), and coastal erosion (Barnard et al., 2017). Additionally, waves impact ocean circulation and mediate interactions between air and sea (Donelan et al., 1997) as well as sea-ice interactions (Thomas et al., 2019). Studying these long-term and interannual changes demands precise time series data spanning several decades. Until now, such records have been available only from global model reanalyses or localised in situ observations. While buoy data are valuable, they offer limited local insights and are especially scarce in the southern hemisphere. In contrast, altimeters deliver global, high-quality measurements of significant wave heights (SWH) (Gommenginger et al., 2002). The growing satellite record of SWH now facilitates more extensive global and long-term analyses. By using SWH data from a multi-mission altimetric product from 2002 to 2020, we can calculate global mean SWH and extreme SWH and evaluate their trends.\n\n**KEY FINDINGS**\n\nOver the period from 2002 to 2020, positive trends in both Significant Wave Height (SWH) and extreme SWH are mostly found in the southern hemisphere. The 95th percentile of wave heights (q95), increases more rapidly than the average values, indicating that extreme waves are growing faster than the average wave height. In the North Atlantic, SWH has increased in summertime (July August September) and decreased during the wintertime: the trend for the 95th percentile SWH is decreasing by 2.1 \u00b1 3.3 cm/year, while the mean SWH shows a decreasing trend of 2.2 \u00b1 1.76 cm/year. In the south of Australia, in boreal winter, the 95th percentile SWH is increasing at a rate of 2.6 \u00b1 1.5 cm/year (a), with the mean SWH increasing by 0.7 \u00b1 0.64 cm/year (b). Finally, in the Antarctic Circumpolar Current, also in boreal winter, the 95th percentile SWH trend is 3.2 \u00b1 2.15 cm/year (a) and the mean SWH trend is 1.4 \u00b1 0.82 cm/year (b). This variation highlights that waves evolve differently across different basins and seasons, illustrating the complex and region-specific nature of wave height trends. A full discussion regarding this OMI can be found in A. Laloue et al. (2024).\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00352\n\n**References:**\n\n* Barnard, P. L., Hoover, D., Hubbard, D. M., Snyder, A., Ludka, B. C., Allan, J., Kaminsky, G. M., Ruggiero, P., Gallien, T. W., Gabel, L., McCandless, D., Weiner, H. M., Cohn, N., Anderson, D. L., and Serafin, K. A.: Extreme oceanographic forcing and coastal response due to the 2015\u20132016 El Ni\u00f1o, Nature Communications, 8, https://doi.org/10.1038/ncomms14365, 2017.\n* Donelan, M. A., Drennan, W. M., and Katsaros, K. B.: The air\u2013sea momentum flux in conditions of wind sea and swell, Journal of Physical Oceanography, 27, 2087\u20132099, https://doi.org/10.1175/1520-0485(1997)0272.0.co;2, 1997.\n* Mentaschi, L., Vousdoukas, M. I., Voukouvalas, E., Dosio, A., and Feyen, L.: Global changes of extreme coastal wave energy fluxes triggered by intensified teleconnection patterns, Geophysical Research Letters, 44, 2416\u20132426, https://doi.org/10.1002/2016gl072488, 2017\n* Thomas, S., Babanin, A. V., Walsh, K. J. E., Stoney, L., and Heil, P.: Effect of wave-induced mixing on Antarctic sea ice in a high-resolution ocean model, Ocean Dynamics, 69, 737\u2013746, https://doi.org/10.1007/s10236-019-01268-0, 2019.\n* Gommenginger, C. P., Srokosz, M. A., Challenor, P. G., and Cotton, P. D.: Development and validation of altimeter wind speed algorithms using an extended collocated Buoy/Topex dataset, IEEE Transactions on Geoscience and Remote Sensing, 40, 251\u2013260, https://doi.org/10.1109/36.992782, 2002.\n* Storlazzi, C. D., Gingerich, S. B., van Dongeren, A., Cheriton, O. M., Swarzenski, P. W., Quataert, E., Voss, C. I., Field, D. W., Annamalai, H., Piniak, G. A., and McCall, R.: Most atolls will be uninhabitable by the mid-21st century because of sea level rise exacerbating wave-driven flooding, Science Advances, 4, https://doi.org/10.1126/sciadv.aap9741, 2018.\n* Husson, R., Charles, E.: EU Copernicus Marine Service Product User Manual for the Global Ocean L 4 Significant Wave Height From Reprocessed Satellite Measurements Product, WAVE_GLO_PHY_SWH_L4_MY_014_007, Issue 2.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-WAV-PUM-014-005-006- 007.pdf, last access: 21 July 2023, 2021 Laloue, A., Ghantous, M., Faug\u00e8re, Y., Dalphinet. A., Aouf, L.: Statistical analysis of global ocean significant wave heights from satellite altimetry over the past two decades. OSR-8 (under review)\n", "doi": "10.48670/mds-00352", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-seastate-global-swh-mean-and-p95-obs,satellite-observation,sea-surface-significant-height,sea-surface-significant-height-seasonal-number-of-observations,sea-surface-significant-height-trend-uncertainty-95percentile,sea-surface-significant-height-trend-uncertainty-mean,sea-surface-wave-significant-height-95percentile-trend,sea-surface-wave-significant-height-mean-trend,sea-surface-wave-significant-height-seasonal-95percentile,sea-surface-wave-significant-height-seasonal-mean,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2002-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean, extreme and mean significant wave height trends from satellite observations - seasonal trends"}, "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_baltic_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\n**CONTEXT**\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Baltic Sea is affected by vertical land motion due to the Glacial Isostatic Adjustment (Ludwigsen et al., 2020) and consequently relative sea level trends (as measured by tide gauges) have been shown to be strongly negative, especially in the northern part of the basin. On the other hand, Baltic Sea absolute sea level trends (from altimetry-based observations) show statistically significant positive trends (Passaro et al., 2021).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\nUp to 45 stations fulfill the completeness index criteria in this region, a few less than in 2020 (51). The spatial variation of the mean 99th percentiles follow the tidal range pattern, reaching its highest values in the northern end of the Gulf of Bothnia (e.g.: 0.81 and 0.78 m above mean sea level at the Finnish stations Kemi and Oulu, respectively) and the inner part of the Gulf of Finland (e.g.: 0.83 m above mean sea level in St. Petersburg, Russia). Smaller tides and therefore 99th percentiles are found along the southeastern coast of Sweden, between Stockholm and Gotland Island (e.g.: 0.42 m above mean sea level in Visby, Gotland Island-Sweden). Annual 99th percentiles standard deviation ranges between 3-5 cm in the South (e.g.: 3 cm in Korsor, Denmark) to 10-13 cm in the Gulf of Finland (e.g.: 12 cm in Hamina). Negative anomalies of 2022 99th percentile are observed in the northern part of the basin, in the Gulf of Bothnia, in the inner part of the Gulf of Finland and in Lolland Island stations (Denmark) reaching maximum values of -12 cm in Kemi, -9 cm in St. Petersburg and -8 cm in Rodby, respectively.. Positive anomalies of 2022 99th percentile are however found in the central and southeastern parts of the basin, with maximum values reaching 7 cm in Paldisky (Estonia) and Slipshavn (Denmark). \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00203\n\n**References:**\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1. Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Passaro M, M\u00fcller F L, Oelsmann J, Rautiainen L, Dettmering D, Hart-Davis MG, Abulaitijiang A, Andersen, OB, H\u00f8yer JL, Madsen, KS, Ringgaard IM, S\u00e4rkk\u00e4 J, Scarrott R, Schwatke C, Seitz F, Tuomi L, Restano M, and Benveniste J. 2021. Absolute Baltic Sea Level Trends in the Satellite Altimetry Era: A Revisit, Front Mar Sci, 8, 647607, https://doi.org/10.3389/FMARS.2021.647607/BIBTEX.\n", "doi": "10.48670/moi-00203", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sl-baltic-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_ibi_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\n**CONTEXT**\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Iberian Biscay Ireland region shows positive sea level trend modulated by decadal-to-multidecadal variations driven by ocean dynamics and superposed to the long-term trend (Chafik et al., 2019).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe completeness index criteria is fulfilled by 57 stations in 2021, two more than those available in 2021 (55), recently added to the multi-year product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles reflect the great tide spatial variability around the UK and the north of France. Minimum values are observed in the Irish eastern coast (e.g.: 0.66 m above mean sea level in Arklow Harbour) and the Canary Islands (e.g.: 0.93 and 0.96 m above mean sea level in Gomera and Hierro, respectively). Maximum values are observed in the Bristol and English Channels (e.g.: 6.26, 5.58 and 5.17 m above mean sea level in Newport, St. Malo and St. Helier, respectively). The annual 99th percentiles standard deviation reflects the south-north increase of storminess, ranging between 1-2 cm in the Canary Islands to 12 cm in Newport (Bristol Channel). Although less pronounced and general than in 2021, negative or close to zero anomalies of 2022 99th percentile still prevail throughout the region this year reaching up to -14 cm in St.Helier (Jersey Island, Channel Islands), or -12 cm in St. Malo. Positive anomalies of 2022 99th percentile are found in the northern part of the region (Irish eastern coast and west Scotland coast) and at a couple of stations in Southern England, with values reaching 9 cm in Bangor (Northern Ireland) and 6 cm in Portsmouth (South England). \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00253\n\n**References:**\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1. Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Chafik L, Nilsen JE\u00d8, Dangendorf S et al. 2019. North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era. Sci Rep 9, 1041. https://doi.org/10.1038/s41598-018-37603-6\n", "doi": "10.48670/moi-00253", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sl-ibi-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_medsea_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Mediterranean Sea shows statistically significant positive sea level trends over the whole basin. However, at sub-basin scale sea level trends show spatial variability arising from local circulation (Calafat et al., 2022; Meli et al., 2023).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe completeness index criteria is fulfilled in this region by 38 stations, 26 more than in 2021, significantly increasing spatial coverage with new in situ data in the central Mediterranean Sea, primarily from Italian stations. The mean 99th percentiles reflect the spatial variability of the tide, a microtidal regime, along the Spanish, French and Italian coasts, ranging from around 0.20 m above mean sea level in Sicily and the Balearic Islands (e.g.: 0.22 m in Porto Empedocle, 0.23 m in Ibiza)) to around 0.60 m above mean sea level in the Northern Adriatic Sea (e.g.: 0.63 m in Trieste, 0.61 m in Venice). . The annual 99th percentiles standard deviation ranges between 2 cm in M\u00e1laga and Motril (South of Spain) to 8 cm in Marseille. . The 2022 99th percentile anomalies present negative values mainly along the Spanish coast (as in 2021) and in the islands of Corsica and Sardinia (Western part of the region), while positive values are observed along the Eastern French Mediterranean coast and at most of the Italian stations (closer to the central part of the region), with values ranging from -4 cm in M\u00e1laga and Motril (Spain) to +5 cm in Ancona (Italy). \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00265\n\n**References:**\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Calafat, F. M., Frederikse, T., and Horsburgh, K.: The Sources of Sea-Level Changes in the Mediterranean Sea Since 1960, J Geophys Res Oceans, 127, e2022JC019061, https://doi.org/10.1029/2022JC019061, 2022.\n* Legeais J-F, Llovel W, Melet A, and Meyssignac B. 2020. Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, s77\u2013s82, https://doi.org/10.1080/1755876X.2020.1785097.\n* Meli M, Camargo CML, Olivieri M, Slangen ABA, and Romagnoli C. 2023. Sea-level trend variability in the Mediterranean during the 1993\u20132019 period, Front Mar Sci, 10, 1150488, https://doi.org/10.3389/FMARS.2023.1150488/BIBTEX.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n", "doi": "10.48670/moi-00265", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-extreme-sl-medsea-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_northwestshelf_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\n**CONTEXT**\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one metre by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe North West Shelf area presents positive sea level trends with higher trend estimates in the German Bight and around Denmark, and lower trends around the southern part of Great Britain (Dettmering et al., 2021).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe completeness index criteria is fulfilled in this region by 34 stations, eight more than in 2021 (26), most of them from Norway. The mean 99th percentiles present a large spatial variability related to the tidal pattern, with largest values found in East England and at the entrance of the English channel, and lowest values along the Danish and Swedish coasts, ranging from the 3.08 m above mean sea level in Immingan (East England) to 0.57 m above mean sea level in Ringhals (Sweden) and Helgeroa (Norway). The standard deviation of annual 99th percentiles ranges between 2-3 cm in the western part of the region (e.g.: 2 cm in Harwich, 3 cm in Dunkerke) and 7-8 cm in the eastern part and the Kattegat (e.g. 8 cm in Stenungsund, Sweden).. The 99th percentile anomalies for 2022 show positive values in Southeast England, with a maximum value of +8 cm in Lowestoft, and negative values in the eastern part of the Kattegat, reaching -8 cm in Oslo. The remaining stations exhibit minor positive or negative values. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00272\n\n**References:**\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Dettmering D, M\u00fcller FL, Oelsmann J, Passaro M, Schwatke C, Restano M, Benveniste J, and Seitz F. 2021. North SEAL: A new dataset of sea level changes in the North Sea from satellite altimetry, Earth Syst Sci Data, 13, 3733\u20133753, https://doi.org/10.5194/ESSD-13-3733-2021.\n", "doi": "10.48670/moi-00272", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-extreme-sl-northwestshelf-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Baltic Sea has showed in the last two decades a warming trend across the whole basin with more frequent and severe heat waves (IPCC, 2022). This trend is significantly higher when considering only the summer season, which would affect the high extremes (e.g. H\u00f8yer and Karagali, 2016).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles showed in the area go from 19.6\u00baC in Tallinn station to 21.4\u00baC in Rohukula station, and the standard deviation ranges between 1\u00baC and 5.4\u00baC reached in the Estonian Coast.\nResults for this year show either positive or negative low anomalies in the Coast of Sweeden (-0.7/+0.5\u00baC) within the standard deviation margin and a general positive anomaly in the rest of the region. This anomaly is noticeable in Rohukula and Virtsu tide gauges (Estonia) with +3.9\u00baC, but inside the standard deviation in both locations. In the South Baltic two stations, GreifswalderOie and Neustadt, reach an anomaly of +2\u00baC, but around the standard deviation.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00204\n\n**References:**\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541. https://doi.org/10.1175/JCLI-D-15-0663.1\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00204", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sst-baltic-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Iberia Biscay Ireland area is characterized by a great complexity in terms of processes that take place in it. The sea surface temperature varies depending on the latitude with higher values to the South. In this area, the clear warming trend observed in other European Seas is not so evident. The northwest part is influenced by the refreshing trend in the North Atlantic, and a mild warming trend has been observed in the last decade (Pisano et al. 2020).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles showed in the area present a range from 16-20\u00baC in the Southwest of the British Isles and the English Channel, 19-21\u00baC in the West of Galician Coast, 21-23\u00baC in the south of Bay of Biscay, 23.5\u00baC in the Gulf of Cadiz to 24.5\u00baC in the Canary Island. The standard deviations are between 0.5\u00baC and 1.3\u00baC in the region except in the English Channel where the standard deviation is higher, reaching 3\u00baC.\nResults for this year show either positive or negative low anomalies below the 45\u00ba parallel, with a slight positive anomaly in the Gulf of Cadiz and the Southeast of the Bay of Biscay over 1\u00baC. In the Southwest of the British Isles and the English Channel, the anomaly is clearly positive, with some stations with an anomaly over 2\u00baC, but inside the standard deviation in the area. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00255\n\n**References:**\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Nardelli BB. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sensing 12(132). DOI: 10.3390/rs12010132.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00255", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sst-ibi-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann et al., 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Mediterranean Sea has showed a constant increase of the SST in the last three decades across the whole basin with more frequent and severe heat waves (Juza et al., 2022). Deep analyses of the variations have displayed a non-uniform rate in space, being the warming trend more evident in the eastern Mediterranean Sea with respect to the western side. This variation rate is also changing in time over the three decades with differences between the seasons (e.g. Pastor et al. 2018; Pisano et al. 2020), being higher in Spring and Summer, which would affect the extreme values.\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles showed in the area present values from 25\u00baC in Ionian Sea and 26\u00ba in the Alboran sea and Gulf of Lion to 27\u00baC in the East of Iberian Peninsula. The standard deviation ranges from 0.6\u00baC to 1.2\u00baC in the Western Mediterranean and is around 2.2\u00baC in the Ionian Sea.\nResults for this year show a slight negative anomaly in the Ionian Sea (-1\u00baC) inside the standard deviation and a clear positive anomaly in the Western Mediterranean Sea reaching +2.2\u00baC, almost two times the standard deviation in the area.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00267\n\n**References:**\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* Pastor F, Valiente JA, Palau JL. 2018. Sea Surface Temperature in the Mediterranean: Trends and Spatial Patterns (1982\u20132016). Pure Appl. Geophys, 175: 4017. https://doi.org/10.1007/s00024-017-1739-z.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Nardelli BB. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sensing 12(132). DOI: 10.3390/rs12010132.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446.\n", "doi": "10.48670/moi-00267", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-extreme-sst-medsea-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\n**CONTEXT**\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe North-West Self area comprises part of the North Atlantic, where this refreshing trend has been observed, and the North Sea, where a warming trend has been taking place in the last three decades (e.g. H\u00f8yer and Karagali, 2016).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\nThe mean 99th percentiles showed in the area present a range from 14-16\u00baC in the North of the British Isles, 16-19\u00baC in the Southwest of the North Sea to 19-21\u00baC around Denmark (Helgoland Bight, Skagerrak and Kattegat Seas). The standard deviation ranges from 0.5-1\u00baC in the North of the British Isles, 0.5-2\u00baC in the Southwest of the North Sea to 1-3\u00baC in the buoys around Denmark.\nResults for this year show either positive or negative low anomalies around their corresponding standard deviation in in the North of the British Isles (-0.5/+0.6\u00baC) and a clear positive anomaly in the other two areas reaching +2\u00baC even when they are around the standard deviation margin.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00274\n\n**References:**\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* H\u00f8yer JL, Karagali I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541. https://doi.org/10.1175/JCLI-D-15-0663.1\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446.\n", "doi": "10.48670/moi-00274", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-extreme-sst-northwestshelf-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nIn the Baltic Sea, the particular bathymetry and geography of the basin intensify the seasonal and spatial fluctuations in wave conditions. No clear statistically significant trend in the sea state has been appreciated except a rising trend in significant wave height in winter season, linked with the reduction of sea ice coverage (Soomere, 2023; Tuomi et al., 2019).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles shown in the area are from 3 to 4 meters and the standard deviation ranges from 0.2 m to 0.4 m. \nResults for this year show a slight positive or negative anomaly in all the stations, from -0.24 m to +0.36 m, inside the margin of the standard deviation.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00199\n\n**References:**\n\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Hochet A, Dodet G, S\u00e9vellec F, Bouin M-N, Patra A, & Ardhuin F. 2023. Time of emergence for altimetry-based significant wave height changes in the North Atlantic. Geophysical Research Letters, 50, e2022GL102348. https://doi.org/10.1029/2022GL102348\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n", "doi": "10.48670/moi-00199", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-wave-baltic-swh-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea significant wave height extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_BLKSEA_recent_changes": {"abstract": "**DEFINITION**\n\nExtreme wave characteristics are computed by analysing single storm events and their long-term means and trends based on the product BLKSEA_MULTIYEAR_WAV_007_006. These storm events were detected using the method proposed by Weisse and G\u00fcnther (2007). The basis of the method is the definition of a severe event threshold (SET), which we define as the 99th percentile of the significant wave height (SWH). Then, the exceedance and shortfall of the SWH at every grid point was determined and counted as a storm event. The analysis of extreme wave events also comprises the following three parameters but are not part of this OMI. The time period between each exceedance and shortfall of the SET is the lifetime of an event. The difference in the maximum SWH of each event and the SET is defined as the event intensity. The geographic area of storm events and exceedance of the SET are defined as the maximum event area. The number, lifetime, and intensity of events were averaged over each year. Finally, the yearly values were used to compute the long-term means. In addition to these parameters, we estimated the difference (anomaly) of the last available year in the multiyear dataset compared against the long-term average as well as the linear trend. To show multiyear variability, each event, fulfilling the above-described definition, is considered in the statistics. This was done independent of the events\u2019 locations within the domain. To obtain long-term trends, a linear regression was applied to the yearly time series. The statistics are based on the period 1950 to -1Y. This approach has been presented in Staneva et al. (2022) for the area of the Black Sea and was later adapted to the South Atlantic in Gramcianinov et al. (2023a, 2023b).\n\n**CONTEXT**\n\nIn the last decade, the European seas have been hit by severe storms, causing serious damage to offshore infrastructure and coastal zones and drawing public attention to the importance of having reliable and comprehensive wave forecasts/hindcasts, especially during extreme events. In addition, human activities such as the offshore wind power industry, the oil industry, and coastal recreation regularly require climate and operational information on maximum wave height at a high resolution in space and time. Thus, there is a broad consensus that a high-quality wave climatology and predictions and a deep understanding of extreme waves caused by storms could substantially contribute to coastal risk management and protection measures, thereby preventing or minimising human and material damage and losses. In this respect and in the frame of climate change, which also affects regional wind patterns and therewith the wave climate, it is important for coastal regions to gain insights into wave extreme characteristics and the related trends. These insights are crucial to initiate necessary abatement strategies especially in combination with extreme wave power statistics (see OMI OMI_EXTREME_WAVE_BLKSEA_wave_power).\n\n**KEY FINDINGS**\n\nThe yearly mean number of storm events is rather low in regions where the average annual lifetime and intensity of storms are high. In contrast, the number of events is high where their lifetime and intensity are low. While the southwest Black Sea is exposed to yearly mean storm event numbers of below the long-term spatial averages (7.3 events), it is observed that the yearly mean lifetime of the events in the same region is higher than the long-term averages. The extreme wave statistics based on the 99th percentile threshold of the significant wave height (SWH) are very similar to the wind sea wave parameter, and the swell contribution is much lower. On overall, the yearly trend of the storm events is slightly negative (-0.01 events/year) with two areas showing positive trends located in the very east and west. In terms of the mean number of storm events in 2022, a pronounced area with positive values is located along the eastern coast and another in the western basin. The rest of the Black Sea area mostly experienced less events in 2022.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00348\n\n**References:**\n\n* Gramcianinov, C.B., Staneva, J., de Camargo, R., & da Silva Dias, P.L. (2023a): Changes in extreme wave events in the southwestern South Atlantic Ocean. Ocean Dynamics, doi:10.1007/s10236-023-01575-7\n* Gramcianinov, C.B., Staneva, J., Souza, C.R.G., Linhares, P., de Camargo, R., & da Silva Dias, P.L. (2023b): Recent changes in extreme wave events in the south-western South Atlantic. In: von Schuckmann, K., Moreira, L., Le Traon, P.-Y., Gr\u00e9goire, M., Marcos, M., Staneva, J., Brasseur, P., Garric, G., Lionello, P., Karstensen, J., & Neukermans, G. (eds.): 7th edition of the Copernicus Ocean State Report (OSR7). Copernicus Publications, State Planet, 1-osr7, 12, doi:10.5194/sp-1-osr7-12-2023\n* Staneva, J., Ricker, M., Akp\u0131nar, A., Behrens, A., Giesen, R., & von Schuckmann, K. (2022): Long-term interannual changes in extreme winds and waves in the Black Sea. Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography, 15:suppl, 1-220, S.2.8., 64-72, doi:10.1080/1755876X.2022.2095169\n* Weisse, R., & G\u00fcnther, H. (2007): Wave climate and long-term changes for the Southern North Sea obtained from a high-resolution hindcast 1958\u20132002. Ocean Dynamics, 57(3), 161\u2013172, doi:10.1007/s10236-006-0094-x\n", "doi": "10.48670/mds-00348", "instrument": null, "keywords": "2022-anomaly-of-yearly-mean-number-of-wave-storm-events,black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-extreme-wave-blksea-recent-changes,swh,weather-climate-and-seasonal-forecasting,wind-speed,yearly-mean-number-of-wave-storm-events,yearly-trend-of-mean-number-of-wave-storm-events", "license": "proprietary", "missionStartDate": "1986-01-30T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea extreme wave events"}, "OMI_EXTREME_WAVE_BLKSEA_wave_power": {"abstract": "**DEFINITION**\n\nThe Wave Power P is defined by:\nP=(\u03c1g^2)/64\u03c0 H_s^2 T_e\nWhere \u03c1 is the surface water density, g the acceleration due to gravity, Hs the significant wave height (VHM0), and Te the wave energy period (VTM10) also abbreviated with Tm-10. The extreme statistics and related recent changes are defined by (1) the 99th percentile of the Wave Power, (2) the linear trend of 99th percentile of the Wave Power, and (3) the difference (anomaly) of the 99th percentile of the last available year in the multiyear dataset BLKSEA_MULTIYEAR_WAV_007_006 compared against the long-term average. The statistics are based on the period 1950 to -1Y and are obtained from yearly averages. This approach has been presented in Staneva et al. (2022).\n\n**CONTEXT**\n\nIn the last decade, the European seas have been hit by severe storms, causing serious damage to offshore infrastructure and coastal zones and drawing public attention to the importance of having reliable and comprehensive wave forecasts/hindcasts, especially during extreme events. In addition, human activities such as the offshore wind power industry, the oil industry, and coastal recreation regularly require climate and operational information on maximum wave height at a high resolution in space and time. Thus, there is a broad consensus that a high-quality wave climatology and predictions and a deep understanding of extreme waves caused by storms could substantially contribute to coastal risk management and protection measures, thereby preventing or minimising human and material damage and losses. In this respect, the Wave Power is a crucial quantity to plan and operate wave energy converters (WEC) and for coastal and offshore structures. For both reliable estimates of long-term Wave Power extremes are important to secure a high efficiency and to guarantee a robust and secure design, respectively.\n\n**KEY FINDINGS**\n\nThe 99th percentile of wave power mean patterns are overall consistent with the respective significant wave height pattern. The maximum 99th percentile of wave power is observed in the southwestern Black Sea. Typical values of in the eastern basin are ~20 kW/m and in the western basin ~45 kW/m. The trend of the 99th percentile of the wave power is decreasing with typical values of 50 W/m/year and a maximum of 120 W/m/year, which is equivalent to a ~25% decrease over whole period with respect to the mean. The pattern of the anomaly of the 99th percentile of wave power in 2022 correlates well with that of the wind speed anomaly in 2022, revealing a negative wave-power anomaly in the western Black Sea (P_20200.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was negative (-0.21% year-1) inside the North Atlantic gyre relative to 2000-01-01 values. This is a slightly lower rate of change compared with the -0.24% trend for the 1997-2020 period but is still statistically significant (p<0.05).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00226\n\n**References:**\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00226", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-nag-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean": {"abstract": "**DEFINITION**\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data inside the gyre. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\n**CONTEXT**\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\n**CMEMS KEY FINDINGS**\n\nThe trend in the North Pacific gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 1.75% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 2.17% reported for the 1997-2020 period. The trend is statistically significant (p<0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was negative (-0.26% year-1) in the North Pacific gyre relative to 2000-01-01 values. This trend is slightly less negative than the trend of -0.31% year-1 for the 1997-2020 period, though the sign of the trend remains unchanged and is statistically significant (p<0.05). It must be noted that the difference is small and within the uncertainty of the calculations, indicating that the trend is significant, however there may be no change associated with the timeseries extension.\nFor 2016, The Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00227\n\n**References:**\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00227", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-npg-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean": {"abstract": "**DEFINITION**\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data inside the gyre. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\n**CONTEXT**\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\n**CMEMS KEY FINDINGS**\n\nThe trend in the South Altantic gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.01% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.09% rate for the 1997-2020 trend (though within the uncertainties associated with the two estimates) and is not statistically significant (p>0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was positive (0.73% year-1) relative to 2000-01-01 values. This is a significant increase from the trend of 0.35% year-1 for the 1997-2020 period and is statistically significant (p<0.05). The last two years of the timeseries show an increased deviation from the mean.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00228\n\n**References:**\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00228", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-sag-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "South Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean": {"abstract": "**DEFINITION**\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\n**CONTEXT**\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\n**CMEMS KEY FINDINGS**\n\nThe trend in the South Pacific gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.04% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.16% change for the 1997-2020 period, with the sign of the trend remaining unchanged and is not statistically significant (p<0.05). An underlying low frequency signal is observed with a period of approximately a decade.\nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was positive (0.66% year-1) in the South Pacific gyre relative to 2000-01-01 values. This rate has increased compared to the rate of 0.45% year-1 for the 1997-2020 period and remains statistically significant (p<0.05). In the last two years of the timeseries, an increase in the variation from the mean is observed.\nFor 2016, the Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00229\n\n**References:**\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00229", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-spg-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "South Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend": {"abstract": "**DEFINITION**\n\nThe trend map is derived from version 5 of the global climate-quality chlorophyll time series produced by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020) and distributed by CMEMS. The trend detection method is based on the Census-I algorithm as described by Vantrepotte et al. (2009), where the time series is decomposed as a fixed seasonal cycle plus a linear trend component plus a residual component. The linear trend is expressed in % year -1, and its level of significance (p) calculated using a t-test. Only significant trends (p < 0.05) are included. \n\n**CONTEXT**\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration is the most widely used measure of the concentration of phytoplankton present in the ocean. Drivers for chlorophyll variability range from small-scale seasonal cycles to long-term climate oscillations and, most importantly, anthropogenic climate change. Due to such diverse factors, the detection of climate signals requires a long-term time series of consistent, well-calibrated, climate-quality data record. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\n**CMEMS KEY FINDINGS**\n\nThe average global trend for the 1997-2021 period was 0.51% per year, with a maximum value of 25% per year and a minimum value of -6.1% per year. Positive trends are pronounced in the high latitudes of both northern and southern hemispheres. The significant increases in chlorophyll reported in 2016-2017 (Sathyendranath et al., 2018b) for the Atlantic and Pacific oceans at high latitudes appear to be plateauing after the 2021 extension. The negative trends shown in equatorial waters in 2020 appear to be remain consistent in 2021. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00230\n\n**References:**\n\n* Jackson, T. (2020) OC-CCI Product User Guide (PUG). ESA/ESRIN Report. D4.2PUG, 2020-10-12. Issue:v4.2. https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018b, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sathyendranath, S, Brewin, RJW, Brockmann, C, Brotas, V, Calton, B, Chuprin, A, Cipollini, P, Couto, AB, Dingle, J, Doerffer, R, Donlon, C, Dowell, M, Farman, A, Grant, M, Groom, S, Horseman, A, Jackson, T, Krasemann, H, Lavender, S, Martinez-Vicente, V, Mazeran, C, M\u00e9lin, F, Moore, TS, Mu\u0308ller, D, Regner, P, Roy, S, Steele, CJ, Steinmetz, F, Swinton, J, Taberner, M, Thompson, A, Valente, A, Zu\u0308hlke, M, Brando, VE, Feng, H, Feldman, G, Franz, BA, Frouin, R, Gould, Jr., RW, Hooker, SB, Kahru, M, Kratzer, S, Mitchell, BG, Muller-Karger, F, Sosik, HM, Voss, KJ, Werdell, J, and Platt, T (2019) An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors: 19, 4285. doi:10.3390/s19194285\n* Vantrepotte, V., M\u00e9lin, F., 2009. Temporal variability of 10-year global SeaWiFS time series of phytoplankton chlorophyll-a concentration. ICES J. Mar. Sci., 66, 1547-1556. doi: 10.1093/icesjms/fsp107.\n", "doi": "10.48670/moi-00230", "instrument": null, "keywords": "change-in-mass-concentration-of-chlorophyll-in-seawater-over-time,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean": {"abstract": "**DEFINITION**\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend.\n\n**CONTEXT**\n\nPhytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Mediterranean Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) and El Nin\u0303o Southern Oscillation (ENSO) (Basterretxea et al. 2018, Colella et al. 2016).\n\n**KEY FINDINGS**\n\nIn the Mediterranean Sea, the trend average for the 1997-2023 period is slightly negative (-0.73\u00b10.65% per year) emphasising the results obtained from previous release (1997-2022). This result is in contrast with the analysis of Sathyendranath et al. (2018) that reveals an increasing trend in chlorophyll concentration in all the European Seas. Starting from 2010-2011, except for 2018-2019, the decrease of chlorophyll concentrations is quite evident in the deseasonalized timeseries (in green), and in the maxima of the observations (grey line), starting from 2015. This attenuation of chlorophyll values of the last decade, results in an overall negative trend for the Mediterranean Sea.\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00259\n\n**References:**\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Berthon, J.-F., Zibordi, G. (2004). Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., Santoleri, R., 2016. Mediterranean ocean colour chlorophyll trends. PLoS One 11, 1 16. https://doi.org/10.1371/journal.pone.0155756.\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. Quality Information Document. Copernicus Marine Service. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. (https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf).\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379 1389.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00259", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-health-chl-medsea-oceancolour-area-averaged-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend": {"abstract": "**DEFINITION**\n\nThis product includes the Mediterranean Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) (at 1 km resolution) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). \nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included.\n\n**CONTEXT**\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). The Mediterranean Sea is an oligotrophic basin, where chlorophyll concentration decreases following a specific gradient from West to East (Colella et al. 2016). The highest concentrations are observed in coastal areas and at the river mouths, where the anthropogenic pressure and nutrient loads impact on the eutrophication regimes (Colella et al. 2016). The the use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\n**KEY FINDINGS**\n\nChlorophyll trend in the Mediterranean Sea, for the period 1997-2023, generally confirm trend results of the previous release with negative values over most of the basin. In Ligurian Sea, negative trend is slightly emphasized. As for the previous release, the southern part of the western Mediterranean basin, Rhode Gyre and in the northern coast of the Aegean Sea show weak positive trend areas but they seems weaker than previous ones. On average the trend in the Mediterranean Sea is about -0.83% per year, emphasizing the mean negative trend achieved in the previous release. Contrary to what shown by Salgado-Hernanz et al. (2019) in their analysis (related to 1998-2014 satellite observations), western and eastern part of the Mediterranean Sea do not show differences. In the Ligurian Sea, the trend switch to negative values, differing from the positive regime observed in the trend maps of both Colella et al. (2016) and Salgado-Hernanz et al. (2019), referred, respectively, to 1998-2009 and 1998-2014 period, respectively. The waters offshore the Po River mouth show weak negative trend values, partially differing from the markable negative regime observed in the 1998-2009 period (Colella et al., 2016), and definitely moving from the positive trend observed by Salgado-Hernanz et al. (2019).\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00260\n\n**References:**\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 200.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-038to045-071-073-078-079-095-096.pdf).\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42.\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Salgado-Hernanz, P. M., Racault, M. F., Font-Mu\u00f1oz, J. S., & Basterretxea, G. (2019). Trends in phytoplankton phenology in the Mediterranean Sea based on ocean-colour remote sensing. Remote Sensing of Environment, 221, 50-64.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00260", "instrument": null, "keywords": "change-in-mass-concentration-of-chlorophyll-in-seawater-over-time,coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-health-chl-medsea-oceancolour-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean": {"abstract": "**DEFINITION**\n\nThe Mediterranean water mass formation rates are evaluated in 4 areas as defined in the Ocean State Report issue 2 section 3.4 (Simoncelli and Pinardi, 2018) as shown in Figure 2: (1) the Gulf of Lions for the Western Mediterranean Deep Waters (WMDW); (2) the Southern Adriatic Sea Pit for the Eastern Mediterranean Deep Waters (EMDW); (3) the Cretan Sea for Cretan Intermediate Waters (CIW) and Cretan Deep Waters (CDW); (4) the Rhodes Gyre, the area of formation of the so-called Levantine Intermediate Waters (LIW) and Levantine Deep Waters (LDW).\nAnnual water mass formation rates have been computed using daily mixed layer depth estimates (density criteria \u0394\u03c3 = 0.01 kg/m3, 10 m reference level) considering the annual maximum volume of water above mixed layer depth with potential density within or higher the specific thresholds specified in Table 1 then divided by seconds per year.\nAnnual mean values are provided using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al. 2020, 2021).\n\n**CONTEXT**\n\nThe formation of intermediate and deep water masses is one of the most important processes occurring in the Mediterranean Sea, being a component of its general overturning circulation. This circulation varies at interannual and multidecadal time scales and it is composed of an upper zonal cell (Zonal Overturning Circulation) and two main meridional cells in the western and eastern Mediterranean (Pinardi and Masetti 2000).\nThe objective is to monitor the main water mass formation events using the eddy resolving Mediterranean Sea Reanalysis (Escudier et al. 2020, 2021) and considering Pinardi et al. (2015) and Simoncelli and Pinardi (2018) as references for the methodology. The Mediterranean Sea Reanalysis can reproduce both Eastern Mediterranean Transient and Western Mediterranean Transition phenomena and catches the principal water mass formation events reported in the literature. This will permit constant monitoring of the open ocean deep convection process in the Mediterranean Sea and a better understanding of the multiple drivers of the general overturning circulation at interannual and multidecadal time scales. \nDeep and intermediate water formation events reveal themselves by a deep mixed layer depth distribution in four Mediterranean areas (Table 1 and Figure 2): Gulf of Lions, Southern Adriatic Sea Pit, Cretan Sea and Rhodes Gyre. \n\n**CMEMS KEY FINDINGS**\n\nThe Western Mediterranean Deep Water (WMDW) formation events in the Gulf of Lion appear to be larger after 1999 consistently with Schroeder et al. (2006, 2008) related to the Eastern Mediterranean Transient event. This modification of WMDW after 2005 has been called Western Mediterranean Transition. WMDW formation events are consistent with Somot et al. (2016) and the event in 2009 is also reported in Houpert et al. (2016). \nThe Eastern Mediterranean Deep Water (EMDW) formation in the Southern Adriatic Pit region displays a period of water mass formation between 1988 and 1993, in agreement with Pinardi et al. (2015), in 1996, 1999 and 2000 as documented by Manca et al. (2002). Weak deep water formation in winter 2006 is confirmed by observations in Vilibi\u0107 and \u0160anti\u0107 (2008). An intense deep water formation event is detected in 2012-2013 (Ga\u010di\u0107 et al., 2014). Last years are characterized by large events starting from 2017 (Mihanovic et al., 2021).\nCretan Intermediate Water formation rates present larger peaks between 1989 and 1993 with the ones in 1992 and 1993 composing the Eastern Mediterranean Transient phenomena. The Cretan Deep Water formed in 1992 and 1993 is characterized by the highest densities of the entire period in accordance with Velaoras et al. (2014).\nThe Levantine Deep Water formation rate in the Rhode Gyre region presents the largest values between 1992 and 1993 in agreement with Kontoyiannis et al. (1999). \n\n**Figure caption**\n\nWater mass formation rates [Sverdrup] computed in 4 regions: in the Gulf of Lion for the Western Mediterranean Deep Waters (WMDW); in the Southern Adriatic region for the Eastern Mediterranean Deep Waters (EMDW); in the Cretan Sea for the Cretan Intermediate Waters (CIW) and the Cretan Deep Waters (CDW); in the Rhode Gyre area for the Levantine Intermediate Waters (LIW) and the Levantine Deep Waters (LDW). Product used: MEDSEA_MULTIYEAR_PHY_006_004.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00318\n\n**References:**\n\n* Escudier R., Clementi E., Cipollone A., Pistoia J., Drudi M., Grandi A., Lyubartsev V., Lecci R., Aydogdu A., Delrosso D., Omar M., Masina S., Coppini G., Pinardi N. 2021. A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, Vol.9, pp.1060, DOI:10.3389/feart.2021.702285.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) set. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Ga\u010di\u0107, M., Civitarese, G., Kova\u010devi\u0107, V., Ursella, L., Bensi, M., Menna, M., et al. 2014. Extreme winter 2012 in the Adriatic: an example of climatic effect on the BiOS rhythm. Ocean Sci. 10, 513\u2013522. doi: 10.5194/os-10-513-2014\n* Houpert, L., de Madron, X.D., Testor, P., Bosse, A., D\u2019Ortenzio, F., Bouin, M.N., Dausse, D., Le Goff, H., Kunesch, S., Labaste, M., et al. 2016. Observations of open-ocean deep convection in the northwestern Mediterranean Sea: seasonal and inter- annual variability of mixing and deep water masses for the 2007-2013 period. J Geophys Res Oceans. 121:8139\u20138171. doi:10.1002/ 2016JC011857.\n* Kontoyiannis, H., Theocharis, A., Nittis, K. 1999. Structures and characteristics of newly formed water masses in the NW levantine during 1986, 1992, 1995. In: Malanotte-Rizzoli P., Eremeev V.N., editor. The eastern Mediterranean as a laboratory basin for the assessment of contrasting ecosys- tems. NATO science series (series 2: environmental secur- ity), Vol. 51. Springer: Dordrecht.\n* Manca, B., Kovacevic, V., Gac\u030cic\u0301, M., Viezzoli, D. 2002. Dense water formation in the Southern Adriatic Sea and spreading into the Ionian Sea in the period 1997\u20131999. J Mar Sys. 33/ 34:33\u2013154.\n* Mihanovi\u0107, H., Vilibi\u0107, I., \u0160epi\u0107, J., Mati\u0107, F., Ljube\u0161i\u0107, Z., Mauri, E., Gerin, R., Notarstefano, G., Poulain, P.-M.. 2021. Observation, preconditioning and recurrence of exceptionally high salinities in the Adriatic Sea. Frontiers in Marine Science, Vol. 8, https://www.frontiersin.org/article/10.3389/fmars.2021.672210\n* Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P., ... & Bonaduce, A. 2015. Mediterranean Sea large-scale low-frequency ocean variability and water mass formation rates from 1987 to 2007: a retrospective analysis. Progress in Oceanography, 132, 318-332\n* Schroeder, K., Gasparini, G.P., Tangherlini, M., Astraldi, M. 2006. Deep and intermediate water in the western Mediterranean under the influence of the eastern Mediterranean transient. Geophys Res Lett. 33. doi:10. 1028/2006GL02712.\n* Schroeder, K., Ribotti, A., Borghini, M., Sorgente, R., Perilli, A., Gasparini, G.P. 2008. An extensive western Mediterranean deep water renewal between 2004 and 2006. Geophys Res Lett. 35(18):L18605. doi:10.1029/2008GL035146.\n* Simoncelli, S. and Pinardi, N. 2018. Water mass formation processes in the Mediterranean sea over the past 30 years. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208.\n* Somot, S., Houpert, L., Sevault, F., Testor, P., Bosse, A., Taupier-Letage, I., Bouin, M.N., Waldman, R., Cassou, C., Sanchez-Gomez, E., et al. 2016. Characterizing, modelling and under- standing the climate variability of the deep water formation in the North-Western Mediterranean Sea. Clim Dyn. 1\u201332. doi:10.1007/s00382-016-3295-0.\n* Velaoras, D., Krokos, G., Nittis, K., Theocharis, A. 2014. Dense intermediate water outflow from the Cretan Sea: a salinity driven, recurrent phenomenon, connected to thermohaline circulation changes. J Geophys Res Oceans. 119:4797\u20134820. doi:10.1002/2014JC009937.\n* Vilibic\u0301, I., S\u030cantic\u0301, D. 2008. Deep water ventilation traced by Synechococcus cyanobacteria. Ocean Dyn 58:119\u2013125. doi:10.1007/s10236-008-0135-8.\n* Von Schuckmann K. et al. (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/mds-00318", "instrument": null, "keywords": "coastal-marine-environment,in-situ-ts-profiles,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,omi-var-extreme-wmf-medsea-area-averaged-mean,sea-level,water-mass-formation-rate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1987-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Water Mass Formation Rates from Reanalysis"}, "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012": {"abstract": "For the Antarctic Sea - A sea ice concentration product based on satellite SAR imagery and microwave radiometer data: The algorithm uses SENTINEL-1 SAR EW and IW mode dual-polarized HH/HV data combined with AMSR2 radiometer data.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00320", "doi": "10.48670/mds-00320", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-concentration,sea-ice-edge,seaice-ant-phy-auto-l3-nrt-011-012,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Ocean - High Resolution Sea Ice Information"}, "SEAICE_ANT_PHY_L3_MY_011_018": {"abstract": "Antarctic sea ice displacement during winter from medium resolution sensors since 2002\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00120", "doi": "10.48670/moi-00120", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,oceanographic-geographical-features,satellite-observation,seaice-ant-phy-l3-my-011-018,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-04-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023": {"abstract": "Arctic L3 sea ice product providing concentration, stage-of-development and floe size information retrieved from Sentinel-1 SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm and delivered on a 0.5 km grid.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00343", "doi": "10.48670/mds-00343", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,floe-size;,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-concentration,seaice-arc-phy-auto-l3-mynrt-011-023,stage-of-development,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - High Resolution Sea Ice Information L3"}, "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024": {"abstract": "Arctic L4 sea ice concentration product based on a L3 sea ice concentration product retrieved from Sentinel-1 SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm (SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023), gap-filled with OSI SAF EUMETSAT sea ice concentration products and delivered on a 1 km grid. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00344", "doi": "10.48670/mds-00344", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-concentration,seaice-arc-phy-auto-l4-mynrt-011-024,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - High Resolution Sea Ice Information L4"}, "SEAICE_ARC_PHY_AUTO_L4_NRT_011_015": {"abstract": "For the European Arctic Sea - A sea ice concentration product based on SAR data and microwave radiometer. The algorithm uses SENTINEL-1 SAR EW mode dual-polarized HH/HV data combined with AMSR2 radiometer data. A sea ice type product covering the same area is produced from SENTINEL-1 SAR EW mode dual-polarized HH/HV data.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00122", "doi": "10.48670/moi-00122", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,seaice-arc-phy-auto-l4-nrt-011-015,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - High resolution Sea Ice Concentration and Sea Ice Type"}, "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021": {"abstract": "Arctic Sea and Ice surface temperature\n**Detailed description:** Arctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily supercollated field using all available sensors with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00315", "doi": "10.48670/moi-00315", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,seaice-arc-phy-climate-l3-my-011-021,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016": {"abstract": "Arctic Sea and Ice surface temperature\n\n**Detailed description:**\nArctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00123", "doi": "10.48670/moi-00123", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,seaice-arc-phy-climate-l4-my-011-016,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "SEAICE_ARC_PHY_L3M_NRT_011_017": {"abstract": "For the Arctic Ocean - multiple Sentinel-1 scenes, Sigma0 calibrated and noise-corrected, with individual geographical map projections over Svalbard and Greenland Sea regions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00124", "doi": "10.48670/moi-00124", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,seaice-arc-phy-l3m-nrt-011-017,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ARCTIC Ocean and Sea-Ice Sigma-Nought"}, "SEAICE_ARC_PHY_L4_NRT_011_014": {"abstract": "Arctic sea ice thickness from merged SMOS and Cryosat-2 (CS2) observations during freezing season between October and April. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. On the other hand, CS2 uses radar altimetry to measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00125", "doi": "10.48670/moi-00125", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-arc-phy-l4-nrt-011-014,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "FMI (Finland)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Sea Ice Thickness derived from merging CryoSat-2 and SMOS ice thickness"}, "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010": {"abstract": "Arctic sea ice drift dataset at 3, 6 and 30 day lag during winter. The Arctic low resolution sea ice drift products provided from IFREMER have a 62.5 km grid resolution. They are delivered as daily products at 3, 6 and 30 days for the cold season extended at fall and spring: from September until May, it is updated on a monthly basis. The data are Merged product from radiometer and scatterometer:\n* SSM/I 85 GHz V & H Merged product (1992-1999)\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00126", "doi": "10.48670/moi-00126", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-arc-seaice-l3-rep-observations-011-010,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1991-12-03T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002": {"abstract": "For the Arctic Ocean - The operational sea ice services at MET Norway and DMI provides ice charts of the Arctic area covering Baffin Bay, Greenland Sea, Fram Strait and Barents Sea. The charts show the ice concentration in WMO defined concentration intervals. The three different types of ice charts (datasets) are produced from twice to several times a week: MET charts are produced every weekday. DMI regional charts are produced at irregular intervals daily and a supplemental DMI overview chart is produced twice weekly.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00128", "doi": "10.48670/moi-00128", "instrument": null, "keywords": "arctic-ocean,ca,cb,cc,cd,cf,cn,coastal-marine-environment,concentration-range,ct,data-quality,fa,fb,fc,ice-poly-id-grid,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,polygon-id,polygon-type,sa,satellite-observation,sb,sc,sea-ice-area-fraction,seaice-arc-seaice-l4-nrt-observations-011-002,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea Ice Concentration Charts - Svalbard and Greenland"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007": {"abstract": "The iceberg product contains 4 datasets (IW and EW modes and mosaic for the two modes) describing iceberg concentration as number of icebergs counted within 10x10 km grid cells. The iceberg concentration is derived by applying a Constant False Alarm Rate (CFAR) algorithm on data from Synthetic Aperture Radar (SAR) satellite sensors.\n\nThe iceberg product also contains two additional datasets of individual iceberg positions in Greenland-Newfoundland-Labrador Waters. These datasets are in shapefile format to allow the best representation of the icebergs (the 1st dataset contains the iceberg point observations, the 2nd dataset contains the polygonized satellite coverage). These are also derived by applying a Constant False Alarm Rate (CFAR) algorithm on Sentinel-1 SAR imagery.\nDespite its precision (individual icebergs are proposed), this product is a generic and automated product and needs expertise to be correctly used. For all applications concerning marine navigation, please refer to the national Ice Service of the country concerned.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00129", "doi": "10.48670/moi-00129", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,number-of-icebergs-per-unit-area,oceanographic-geographical-features,satellite-observation,seaice-arc-seaice-l4-nrt-observations-011-007,target-application#seaiceforecastingapplication,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "SAR Sea Ice Berg Concentration and Individual Icebergs Observed with Sentinel-1"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008": {"abstract": "Arctic Sea and Ice surface temperature product based upon observations from the Metop_A AVHRR instrument. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00130", "doi": "10.48670/moi-00130", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,seaice-arc-seaice-l4-nrt-observations-011-008,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea and Ice Surface Temperature"}, "SEAICE_BAL_PHY_L4_MY_011_019": {"abstract": "Gridded sea ice concentration, sea ice extent and classification based on the digitized Baltic ice charts produced by the FMI/SMHI ice analysts. It is produced daily in the afternoon, describing the ice situation daily at 14:00 EET. The nominal resolution is about 1km. The temporal coverage is from the beginning of the season 1980-1981 until today.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00131", "doi": "10.48670/moi-00131", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-classification,sea-ice-concentration,sea-ice-extent,sea-ice-thickness,seaice-bal-phy-l4-my-011-019,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-11-03T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea ice concentration, extent, and classification time series"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004": {"abstract": "For the Baltic Sea- The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The parameters are based on ice chart produced on daily basis during the Baltic Sea ice season and show the ice concentration in a 1 km grid. Ice thickness chart (ITC) is a product based on the most recent available ice chart (IC) and a SAR image. The SAR data is used to update the ice information in the IC. The ice regions in the IC are updated according to a SAR segmentation and new ice thickness values are assigned to each SAR segment based on the SAR backscattering and the ice IC thickness range at that location.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00132\n\n**References:**\n\n* J. Karvonen, M. Simila, SAR-Based Estimation of the Baltic Sea Ice Motion, Proc. of the International Geoscience and Remote Sensing Symposium 2007 (IGARSS 07), pp. 2605-2608, 2007. (Unfortunately there is no publication of the new algorithm version yet).\n", "doi": "10.48670/moi-00132", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-extent,sea-ice-thickness,seaice-bal-seaice-l4-nrt-observations-011-004,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011": {"abstract": "For the Baltic Sea - The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The products are based on SAR images and are produced on pass-by-pass basis during the Baltic Sea ice season, and show the ice thickness and drift in a 500 m and 800m grid, respectively. The Baltic sea ice concentration product is based on data from SAR and microwave radiometer. The algorithm uses SENTINEL-1 SAR EW mode dual-polarized HH/HV data combined with AMSR2 radiometer data.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00133\n\n**References:**\n\n* J. Karvonen, Operational SAR-based sea ice drift monitoring over the Baltic Sea, Ocean Science, v. 8, pp. 473-483, (http://www.ocean-sci.net/8/473/2012/os-8-473-2012.html) 2012.\n", "doi": "10.48670/moi-00133", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-thickness,sea-ice-x-displacement,sea-ice-y-displacement,seaice-bal-seaice-l4-nrt-observations-011-011,target-application#seaiceclimate,target-application#seaiceforecastingapplication,target-application#seaiceinformation,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - SAR Sea Ice Thickness and Drift, Multisensor Sea Ice Concentration"}, "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013": {"abstract": "Arctic sea ice L3 data in separate monthly files. The time series is based on reprocessed radar altimeter satellite data from Envisat and CryoSat and is available in the freezing season between October and April. The product is brokered from the Copernicus Climate Change Service (C3S).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00127", "doi": "10.48670/moi-00127", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-glo-phy-climate-l3-my-011-013,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1995-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea Ice Thickness REPROCESSED"}, "SEAICE_GLO_PHY_L4_MY_011_020": {"abstract": "The product contains a reprocessed multi year version of the daily composite dataset from SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006 covering the Sentinel1 years from autumn 2014 until 1 year before present\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00328", "doi": "10.48670/mds-00328", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-x-displacement,sea-ice-y-displacement,seaice-glo-phy-l4-my-011-020,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - High Resolution SAR Sea Ice Drift Time Series"}, "SEAICE_GLO_PHY_L4_NRT_011_014": {"abstract": "Arctic sea ice thickness from merged L-Band radiometer (SMOS ) and radar altimeter (CryoSat-2, Sentinel-3A/B) observations during freezing season between October and April in the northern hemisphere and Aprilt to October in the southern hemisphere. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. Radar altimeters measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00125", "doi": "10.48670/moi-00125", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-glo-phy-l4-nrt-011-014,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-10-18T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Sea Ice Thickness derived from merging of L-Band radiometry and radar altimeter derived sea ice thickness"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001": {"abstract": "For the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers five global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401, OSI-402, OSI-403, OSI-405 and OSI-408). The sea ice concentration, edge and type products are delivered daily at 10km resolution and the sea ice drift in 62.5km resolution, all in polar stereographic projections covering the Northern Hemisphere and the Southern Hemisphere. The sea ice drift motion vectors have a time-span of 2 days. These are the Sea Ice operational nominal products for the Global Ocean.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00134", "doi": "10.48670/moi-00134", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,sea-ice-x-displacement,sea-ice-y-displacement,seaice-glo-seaice-l4-nrt-observations-011-001,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-10-14T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006": {"abstract": "DTU Space produces polar covering Near Real Time gridded ice displacement fields obtained by MCC processing of Sentinel-1 SAR, Envisat ASAR WSM swath data or RADARSAT ScanSAR Wide mode data . The nominal temporal span between processed swaths is 24hours, the nominal product grid resolution is a 10km.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00135", "doi": "10.48670/moi-00135", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-x-displacement,sea-ice-y-displacement,seaice-glo-seaice-l4-nrt-observations-011-006,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - High Resolution SAR Sea Ice Drift"}, "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009": {"abstract": "The CDR and ICDR sea ice concentration dataset of the EUMETSAT OSI SAF (OSI-450-a and OSI-430-a), covering the period from October 1978 to present, with 16 days delay. It used passive microwave data from SMMR, SSM/I and SSMIS. Sea ice concentration is computed from atmospherically corrected PMW brightness temperatures, using a combination of state-of-the-art algorithms and dynamic tie points. It includes error bars for each grid cell (uncertainties). This version 3.0 of the CDR (OSI-450-a, 1978-2020) and ICDR (OSI-430-a, 2021-present with 16 days latency) was released in November 2022\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00136\n\n**References:**\n\n* [http://osisaf.met.no/docs/osisaf_cdop2_ss2_pum_sea-ice-conc-reproc_v2p2.pdf]\n", "doi": "10.48670/moi-00136", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,seaice-glo-seaice-l4-rep-observations-011-009,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1978-10-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)"}, "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067": {"abstract": "The Mean Dynamic Topography MDT-CMEMS_2020_BLK is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Black Sea. This is consistent with the reference time period also used in the SSALTO DUACS products\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00138", "doi": "10.48670/moi-00138", "instrument": null, "keywords": "black-sea,coastal-marine-environment,invariant,level-4,marine-resources,marine-safety,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-blk-phy-mdt-l4-static-008-067,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "BLACK SEA MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_EUR_PHY_L3_MY_008_061": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n\u201c\u2019Associated products\u201d\u2019\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00139", "doi": "10.48670/moi-00139", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l3-my-008-061,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-10-03T07:53:03Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "SEALEVEL_EUR_PHY_L3_NRT_008_059": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) and 5Hz (~1km) sampling. It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Seas. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n**Associated products**\n\nA time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 [](http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033) describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00140", "doi": "10.48670/moi-00140", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l3-nrt-008-059,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T03:04:52Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA LEVEL ANOMALIES NRT"}, "SEALEVEL_EUR_PHY_L4_MY_008_068": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00141", "doi": "10.48670/moi-00141", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l4-my-008-068,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "SEALEVEL_EUR_PHY_L4_NRT_008_060": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00142", "doi": "10.48670/moi-00142", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l4-nrt-008-060,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070": {"abstract": "The Mean Dynamic Topography MDT-CMEMS_2024_EUR is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the European Seas. This is consistent with the reference time period also used in the SSALTO DUACS products\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00337", "doi": "10.48670/mds-00337", "instrument": null, "keywords": "coastal-marine-environment,invariant,level-4,marine-resources,marine-safety,mediterranean-sea,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-eur-phy-mdt-l4-static-008-070,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057": {"abstract": "DUACS delayed-time altimeter gridded maps of sea surface heights and derived variables over the global Ocean (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global?tab=overview). The processing focuses on the stability and homogeneity of the sea level record (based on a stable two-satellite constellation) and the product is dedicated to the monitoring of the sea level long-term evolution for climate applications and the analysis of Ocean/Climate indicators. These products are produced and distributed by the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/).\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00145", "doi": "10.48670/moi-00145", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-sea-level,sealevel-glo-phy-climate-l4-my-008-057,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "SEALEVEL_GLO_PHY_L3_MY_008_062": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc.). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n**Associated products**\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n**DOI (product)**:\nhttps://doi.org/10.48670/moi-00146", "doi": "10.48670/moi-00146", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l3-my-008-062,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-10-03T01:42:25Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "SEALEVEL_GLO_PHY_L3_NRT_008_044": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) and 5Hz (~1km) sampling. It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n**Associated products**\nA time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 [](http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033) describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n**DOI (product)**:\nhttps://doi.org/10.48670/moi-00147", "doi": "10.48670/moi-00147", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l3-nrt-008-044,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS NRT"}, "SEALEVEL_GLO_PHY_L4_MY_008_047": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00148", "doi": "10.48670/moi-00148", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l4-my-008-047,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "SEALEVEL_GLO_PHY_L4_NRT_008_046": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00149", "doi": "10.48670/moi-00149", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l4-nrt-008-046,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "SEALEVEL_GLO_PHY_MDT_008_063": {"abstract": "Mean Dynamic Topography that combines the global CNES-CLS-2022 MDT, the Black Sea CMEMS2020 MDT and the Med Sea CMEMS2020 MDT. It is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid. This is consistent with the reference time period also used in the DUACS products\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00150", "doi": "10.48670/moi-00150", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,invariant,level-4,marine-resources,marine-safety,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-glo-phy-mdt-008-063,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033": {"abstract": "In wavenumber spectra, the 1hz measurement error is the noise level estimated as the mean value of energy at high wavenumbers (below ~20km in term of wavelength). The 1hz noise level spatial distribution follows the instrumental white-noise linked to the Surface Wave Height but also connections with the backscatter coefficient. The full understanding of this hump of spectral energy (Dibarboure et al., 2013, Investigating short wavelength correlated errors on low-resolution mode altimetry, OSTST 2013 presentation) still remain to be achieved and overcome with new retracking, new editing strategy or new technology.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00144", "doi": "10.48670/moi-00144", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,invariant,level-4,marine-resources,marine-safety,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-sea-level,sealevel-glo-phy-noise-l4-static-008-033,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED NORMALIZED MEASUREMENT NOISE OF SEA LEVEL ANOMALIES"}, "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066": {"abstract": "The Mean Dynamic Topography MDT-CMEMS_2020_MED is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Mediterranean Sea. This is consistent with the reference time period also used in the SSALTO DUACS products\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00151", "doi": "10.48670/moi-00151", "instrument": null, "keywords": "coastal-marine-environment,invariant,level-4,marine-resources,marine-safety,mediterranean-sea,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-med-phy-mdt-l4-static-008-066,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "MEDITERRANEAN SEA MEAN DYNAMIC TOPOGRAPHY"}, "SST_ATL_PHY_L3S_MY_010_038": {"abstract": "For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00311", "doi": "10.48670/moi-00311", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sst-atl-phy-l3s-my-010-038,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Reprocessed"}, "SST_ATL_PHY_L3S_NRT_010_037": {"abstract": "For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02\u00b0 resolution grid. It includes observations by polar orbiting and geostationary satellites . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. 3 more datasets are available that only contain \"per sensor type\" data: Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00310", "doi": "10.48670/moi-00310", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-atl-phy-l3s-nrt-010-037,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-20T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025": {"abstract": "For the Atlantic European North West Shelf Ocean-European North West Shelf/Iberia Biscay Irish Seas. The ODYSSEA NW+IBI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg x 0.02deg horizontal resolution, using satellite data from both infra-red and micro-wave radiometers. It is the sea surface temperature operational nominal product for the Northwest Shelf Sea and Iberia Biscay Irish Seas.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00152", "doi": "10.48670/moi-00152", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-atl-sst-l4-nrt-observations-010-025,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA L4 Sea Surface Temperature Analysis"}, "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026": {"abstract": "For the European North West Shelf Ocean Iberia Biscay Irish Seas. The IFREMER Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.05deg. x 0.05deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00153", "doi": "10.48670/moi-00153", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-atl-sst-l4-rep-observations-010-026,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas - High Resolution L4 Sea Surface Temperature Reprocessed"}, "SST_BAL_PHY_L3S_MY_010_040": {"abstract": "For the Baltic Sea- the DMI Sea Surface Temperature reprocessed L3S aims at providing daily multi-sensor supercollated data at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00312\n\n**References:**\n\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00312", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-phy-l3s-my-010-040,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - L3S Sea Surface Temperature Reprocessed"}, "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034": {"abstract": "For the Baltic Sea - the DMI Sea Surface Temperature Diurnal Subskin L4 aims at providing hourly analysis of the diurnal subskin signal at 0.02deg. x 0.02deg. horizontal resolution, using the BAL L4 NRT product as foundation temperature and satellite data from infra-red radiometers. Uses SST satellite products from the sensors: Metop B AVHRR, Sentinel-3 A/B SLSTR, VIIRS SUOMI NPP & NOAA20 \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00309\n\n**References:**\n\n* Karagali I. and H\u00f8yer, J. L. (2014). Characterisation and quantification of regional diurnal cycles from SEVIRI. Ocean Science, 10 (5), 745-758.\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00309", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-phy-subskin-l4-nrt-010-034,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-05-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032": {"abstract": "For the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00154\n\n**References:**\n\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00154", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-sst-l3s-nrt-observations-010-032,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-03-11T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Sea/Baltic Sea - Sea Surface Temperature Analysis L3S"}, "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b": {"abstract": "For the Baltic Sea- The DMI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red and microwave radiometers. Uses SST nighttime satellite products from these sensors: NOAA AVHRR, Metop AVHRR, Terra MODIS, Aqua MODIS, Aqua AMSR-E, Envisat AATSR, MSG Seviri\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00155", "doi": "10.48670/moi-00155", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-sst-l4-nrt-observations-010-007-b,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea- Sea Surface Temperature Analysis L4"}, "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016": {"abstract": "For the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. The product uses SST satellite products from the ESA CCI and Copernicus C3S projects, including the sensors: NOAA AVHRRs 7, 9, 11, 12, 14, 15, 16, 17, 18 , 19, Metop, ATSR1, ATSR2, AATSR and SLSTR.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00156\n\n**References:**\n\n* H\u00f8yer, J. L., & Karagali, I. (2016). Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00156", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,sst-bal-sst-l4-rep-observations-010-016,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea- Sea Surface Temperature Reprocessed"}, "SST_BS_PHY_L3S_MY_010_041": {"abstract": "The Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00313\n\n**References:**\n\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18. Pisano, A., Buongiorno Nardelli, B., Tronconi, C. & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sens. Environ. 176, 107\u2013116.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n", "doi": "10.48670/moi-00313", "instrument": null, "keywords": "adjusted-sea-surface-temperature,black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sst-bs-phy-l3s-my-010-041,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "SST_BS_PHY_SUBSKIN_L4_NRT_010_035": {"abstract": "For the Black Sea - the CNR diurnal sub-skin Sea Surface Temperature product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Black Sea (BS) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS BS Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00157\n\n**References:**\n\n* Marullo, S., Santoleri, R., Ciani, D., Le Borgne, P., P\u00e9r\u00e9, S., Pinardi, N., ... & Nardone, G. (2014). Combining model and geostationary satellite data to reconstruct hourly SST field over the Mediterranean Sea. Remote sensing of environment, 146, 11-23.\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00157", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,sst-bs-phy-subskin-l4-nrt-010-035,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013": {"abstract": "For the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Black Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00158\n\n**References:**\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00158", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-bs-sst-l3s-nrt-observations-010-013,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006": {"abstract": "For the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain providess daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Black Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00159\n\n**References:**\n\n* Buongiorno Nardelli B., S. Colella, R. Santoleri, M. Guarracino, A. Kholod, 2009: A re-analysis of Black Sea Surface Temperature, J. Mar. Sys.., doi:10.1016/j.jmarsys.2009.07.001\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00159", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bs-sst-l4-nrt-observations-010-006,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "SST_BS_SST_L4_REP_OBSERVATIONS_010_022": {"abstract": "The Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00160\n\n**References:**\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "doi": "10.48670/moi-00160", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bs-sst-l4-rep-observations-010-022,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "SST_GLO_PHY_L3S_MY_010_039": {"abstract": "For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. \n\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00329", "doi": "10.48670/mds-00329", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-glo-phy-l3s-my-010-039,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_GLO_PHY_L4_MY_010_044": {"abstract": "For the global ocean. The IFREMER/ODYSSEA Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.10deg. x 0.10deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00345", "doi": "10.48670/mds-00345", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-glo-phy-l4-my-010-044,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean ODYSSEA L4 Sea Surface Temperature"}, "SST_GLO_PHY_L4_NRT_010_043": {"abstract": "This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours.\n\nWhereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed.\n\nThe processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00321", "doi": "10.48670/mds-00321", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-glo-phy-l4-nrt-010-043,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ODYSSEA Global Sea Surface Temperature Gridded Level 4 Daily Multi-Sensor Observations"}, "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010": {"abstract": "For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.1\u00b0 resolution global grid. It includes observations by polar orbiting (NOAA-18 & NOAAA-19/AVHRR, METOP-A/AVHRR, ENVISAT/AATSR, AQUA/AMSRE, TRMM/TMI) and geostationary (MSG/SEVIRI, GOES-11) satellites . The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.3 more datasets are available that only contain \"per sensor type\" data: Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00164", "doi": "10.48670/moi-00164", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-glo-sst-l3s-nrt-observations-010-010,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-20T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001": {"abstract": "For the Global Ocean- the OSTIA global foundation Sea Surface Temperature product provides daily gap-free maps of: Foundation Sea Surface Temperature at 0.05\u00b0 x 0.05\u00b0 horizontal grid resolution, using in-situ and satellite data from both infrared and microwave radiometers. \n\nThe Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the UK's Met Office and delivered by IFREMER PU. OSTIA uses satellite data provided by the GHRSST project together with in-situ observations to determine the sea surface temperature.\nA high resolution (1/20\u00b0 - approx. 6 km) daily analysis of sea surface temperature (SST) is produced for the global ocean and some lakes.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00165\n\n**References:**\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720. doi: 10.3390/rs12040720\n* Donlon, C.J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W., 2012, The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sensing of the Environment. doi: 10.1016/j.rse.2010.10.017 2011.\n* John D. Stark, Craig J. Donlon, Matthew J. Martin and Michael E. McCulloch, 2007, OSTIA : An operational, high resolution, real time, global sea surface temperature analysis system., Oceans 07 IEEE Aberdeen, conference proceedings. Marine challenges: coastline to deep sea. Aberdeen, Scotland.IEEE.\n", "doi": "10.48670/moi-00165", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,sst-glo-sst-l4-nrt-observations-010-001,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2007-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011": {"abstract": "The OSTIA (Good et al., 2020) global sea surface temperature reprocessed product provides daily gap-free maps of foundation sea surface temperature and ice concentration (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal grid resolution, using in-situ and satellite data. This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00168\n\n**References:**\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720, doi:10.3390/rs12040720\n", "doi": "10.48670/moi-00168", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,sst-glo-sst-l4-rep-observations-010-011,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024": {"abstract": "The ESA SST CCI and C3S global Sea Surface Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the (A)ATSRs, SLSTR and the AVHRR series of sensors (Merchant et al., 2019). The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00169\n\n**References:**\n\n* Good, S., Fiedler, E., Mao, C., Martin, M.J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720, doi:10.3390/rs12040720.\n* Lavergne, T., S\u00f8rensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkj\u00e6r, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49-78, doi:10.5194/tc-13-49-2019, 2019.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\n", "doi": "10.48670/moi-00169", "instrument": null, "keywords": "analysed-sst-uncertainty,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-water-temperature,sea-water-temperature-standard-error,sst-glo-sst-l4-rep-observations-010-024,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "SST_MED_PHY_L3S_MY_010_042": {"abstract": "The Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00314\n\n**References:**\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "doi": "10.48670/moi-00314", "instrument": null, "keywords": "adjusted-sea-surface-temperature,coastal-marine-environment,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,satellite-observation,sst-med-phy-l3s-my-010-042,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "SST_MED_PHY_SUBSKIN_L4_NRT_010_036": {"abstract": "For the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n \n[How to cite](https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-reference-copernicus-marine-products-and-services)\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00170\n\n**References:**\n\n* Marullo, S., Santoleri, R., Ciani, D., Le Borgne, P., P\u00e9r\u00e9, S., Pinardi, N., ... & Nardone, G. (2014). Combining model and geostationary satellite data to reconstruct hourly SST field over the Mediterranean Sea. Remote sensing of environment, 146, 11-23.\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00170", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,sst-med-phy-subskin-l4-nrt-010-036,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012": {"abstract": "For the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Mediterranean Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00171\n\n**References:**\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00171", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-med-sst-l3s-nrt-observations-010-012,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004": {"abstract": "For the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Mediterranean Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. Since November 2024, the L4 MED UHR processing chain makes use of an improved background field as initial guess for the Optimal Interpolation of this product. The improvement is obtained in terms of the effective spatial resolution via the application of a convolutional neural network (CNN). These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00172\n\n**References:**\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n* Fanelli, C., Ciani, D., Pisano, A., & Buongiorno Nardelli, B. (2024). Deep Learning for Super-Resolution of Mediterranean Sea Surface Temperature Fields. EGUsphere, 2024, 1-18 (pre-print)\n", "doi": "10.48670/moi-00172", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-med-sst-l4-nrt-observations-010-004,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "SST_MED_SST_L4_REP_OBSERVATIONS_010_021": {"abstract": "The Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00173\n\n**References:**\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "doi": "10.48670/moi-00173", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-med-sst-l4-rep-observations-010-021,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002": {"abstract": "Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra.\nUsing linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered.\nThe dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land).\nThe integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.\nThis product is processed by the WAVE-TAC multi-mission SAR data processing system.\nIt processes near-real-time data from the following missions: SAR (Sentinel-1A and Sentinel-1B) and CFOSAT/SWIM.\nOne file is produced for each mission and is available in two formats depending on the user needs: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00178", "doi": "10.48670/moi-00178", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-fwk-l3-nrt-014-002,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L3_MY_014_006": {"abstract": "Multi-Year mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00174", "doi": "10.48670/moi-00174", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l3-my-014-006,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L3_NRT_014_009": {"abstract": "Near Real-Time mono-mission satellite-based 2D full wave spectral product. These very complete products enable to characterise spectrally the direction, wave length and multiple sea Sates along CFOSAT track (in boxes of 70km/90km left and right from the nadir pointing). The data format are 2D directionnal matrices. They also include integrated parameters (Hs, direction, wavelength) from the spectrum with and without partitions. \n\n**DOI (product):** \nN/A", "doi": null, "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,global-ocean,iberian-biscay-irish-seas,level-3,mediterranean-sea,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l3-nrt-014-009,wave-spectrum", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L4_NRT_014_004": {"abstract": "Near-Real-Time multi-mission global satellite-based spectral integral parameters. Only valid data are used, based on the L3 corresponding product. Included wave parameters are partition significant wave height, partition peak period and partition peak or principal direction. Those parameters are propagated in space and time at a 3-hour timestep and on a regular space grid, providing information of the swell propagation characteristics, from source to land. One file gathers one swell system, gathering observations originating from the same storm source. This product is processed by the WAVE-TAC multi-mission SAR data processing system to serve in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B. All the spectral parameter measurements are optimally interpolated using swell observations belonging to the same swell field. The SAR data processing system produces wave integral parameters by partition (partition significant wave height, partition peak period and partition peak or principal direction) and the associated standard deviation and density of propagated observations. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00175", "doi": "10.48670/moi-00175", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l4-nrt-014-004,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L4 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L3_MY_014_005": {"abstract": "Multi-Year mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle\u2026). All the missions are homogenized with respect to a reference mission and in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.\n\nThis product is based on the ESA Sea State Climate Change Initiative data Level 3 product (version 2) and is formatted by the WAVE-TAC to be homogeneous with the CMEMS Level 3 Near-real-time product. It is based on the reprocessing of GDR data from the following altimeter missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa and Jason-3. CFOSAT Multi-Year dataset is based on the reprocessing of CFOSAT Level-2P products (CNES/CLS), inter-calibrated on Jason-3 reference mission issued from the CCI Sea State dataset.\n\nOne file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH) and the unfiltered SWH (VAVH_UNFILTERED).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00176", "doi": "10.48670/moi-00176", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,wave-glo-phy-swh-l3-my-014-005,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2002-01-15T06:29:22Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L3_NRT_014_001": {"abstract": "Near-Real-Time mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle\u2026). All the missions are homogenized with respect to a reference mission (Jason-3 until April 2022, Sentinel-6A afterwards) and calibrated on in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.\n\nAs a support of information to the significant wave height, wind speed measured by the altimeters is also processed and included in the files. Wind speed values are provided by upstream products (L2) for each mission and are based on different algorithms. Only valid data are included and all the missions are homogenized with respect to the reference mission.\n\nThis product is processed by the WAVE-TAC multi-mission altimeter data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes operational data (OGDR and NRT, produced in near-real-time) from the following altimeter missions: Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Cryosat-2, SARAL/AltiKa, CFOSAT ; and interim data (IGDR, 1 to 2 days delay) from Hai Yang-2B mission.\n\nOne file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH), the unfiltered SWH (VAVH_UNFILTERED) and the wind speed (wind_speed).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00179", "doi": "10.48670/moi-00179", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,wave-glo-phy-swh-l3-nrt-014-001,weather-climate-and-seasonal-forecasting,wind-speed", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L4_MY_014_007": {"abstract": "Multi-Year gridded multi-mission merged satellite significant wave height based on CMEMS Multi-Year level-3 SWH datasets itself based on the ESA Sea State Climate Change Initiative data Level 3 product (see the product WAVE_GLO_PHY_SWH_L3_MY_014_005). Only valid data are included. It merges along-track SWH data from the following missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa, Jason-3 and CFOSAT. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC on a 2\u00b0 horizontal grid ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon) on a 0.5\u00b0 horizontal grid, using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00177", "doi": "10.48670/moi-00177", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,sea-surface-wave-significant-height-daily-maximum,sea-surface-wave-significant-height-daily-mean,sea-surface-wave-significant-height-daily-number-of-observations,sea-surface-wave-significant-height-daily-standard-deviation,sea-surface-wave-significant-height-flag,sea-surface-wave-significant-height-number-of-observations,wave-glo-phy-swh-l4-my-014-007,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2002-01-15T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L4_NRT_014_003": {"abstract": "Near-Real-Time gridded multi-mission merged satellite significant wave height, based on CMEMS level-3 SWH datasets. Onyl valid data are included. It merges multiple along-track SWH data (Sentinel-6A,\u00a0 Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT, SWOT-nadir, HaiYang-2B and HaiYang-2C) and produces daily gridded data at a 2\u00b0 horizontal resolution. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00180", "doi": "10.48670/moi-00180", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,wave-glo-phy-swh-l4-nrt-014-003,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_WAV_L3_SPC_NRT_OBSERVATIONS_014_002": {"abstract": "Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes near-real-time data from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00178", "doi": "10.48670/moi-00178", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-wav-l3-spc-nrt-observations-014-002,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WIND_ARC_PHY_HR_L3_MY_012_105": {"abstract": "For the Arctic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00338", "doi": "10.48670/mds-00338", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-arc-phy-hr-l3-my-012-105,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Arctic Sea"}, "WIND_ARC_PHY_HR_L3_NRT_012_100": {"abstract": "For the Arctic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.'\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00330", "doi": "10.48670/mds-00330", "instrument": null, "keywords": "arctic-ocean,eastward-wind,level-3,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-arc-phy-hr-l3-nrt-012-100,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Arctic Sea"}, "WIND_ATL_PHY_HR_L3_MY_012_106": {"abstract": "For the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00339", "doi": "10.48670/mds-00339", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-atl-phy-hr-l3-my-012-106,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Atlantic Sea"}, "WIND_ATL_PHY_HR_L3_NRT_012_101": {"abstract": "For the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00331", "doi": "10.48670/mds-00331", "instrument": null, "keywords": "eastward-wind,iberian-biscay-irish-seas,level-3,near-real-time,north-west-shelf-seas,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-atl-phy-hr-l3-nrt-012-101,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Atlantic Sea"}, "WIND_BAL_PHY_HR_L3_MY_012_107": {"abstract": "For the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00340", "doi": "10.48670/mds-00340", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-bal-phy-hr-l3-my-012-107,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Baltic Sea"}, "WIND_BAL_PHY_HR_L3_NRT_012_102": {"abstract": "For the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00332", "doi": "10.48670/mds-00332", "instrument": null, "keywords": "baltic-sea,eastward-wind,level-3,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-bal-phy-hr-l3-nrt-012-102,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Baltic Sea"}, "WIND_BLK_PHY_HR_L3_MY_012_108": {"abstract": "For the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00341", "doi": "10.48670/mds-00341", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-blk-phy-hr-l3-my-012-108,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Black Sea"}, "WIND_BLK_PHY_HR_L3_NRT_012_103": {"abstract": "For the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00333", "doi": "10.48670/mds-00333", "instrument": null, "keywords": "black-sea,eastward-wind,level-3,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-blk-phy-hr-l3-nrt-012-103,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Black Sea"}, "WIND_GLO_PHY_CLIMATE_L4_MY_012_003": {"abstract": "For the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT, QuikSCAT SeaWinds and ERS-1 and ERS-2 SCAT satellite instruments. The applied bias corrections, the standard deviation of the differences and the number of observations used to calculate the monthly average persistent bias are included in the product.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00181", "doi": "10.48670/moi-00181", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,global-ocean,iberian-biscay-irish-seas,level-4,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,northward-wind,oceanographic-geographical-features,satellite-observation,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-glo-phy-climate-l4-my-012-003,wind-speed", "license": "proprietary", "missionStartDate": "1994-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_GLO_PHY_L3_MY_012_005": {"abstract": "For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n*0.5 degrees grid for the 50 km scatterometer L2 inputs,\n*0.25 degrees grid based on 25 km scatterometer swath observations,\n*and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. \n\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The MY L3 products follow the availability of the reprocessed EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees;\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00183", "doi": "10.48670/moi-00183", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-glo-phy-l3-my-012-005,wind-speed,wind-to-direction,wvc-index", "license": "proprietary", "missionStartDate": "1991-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "WIND_GLO_PHY_L3_NRT_012_002": {"abstract": "For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n\n*0.5 degrees grid for the 50 km scatterometer L2 inputs,\n*0.25 degrees grid based on 25 km scatterometer swath observations,\n*and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products.\n\nData from ascending and descending passes are gridded separately.\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The NRT L3 products follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for:\n*The ASCAT scatterometers on Metop-A (discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees;\n*The OSCAT scatterometer on Scatsat-1 (discontinued on 28/02/2021) and Oceansat-3 at 0.25 and 0.5 degrees; \n*The HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees\n\nIn addition, the product includes European Centre for Medium-Range Weather Forecasts (ECMWF) operational model forecast wind and stress variables collocated with the scatterometer observations at L2 and processed to L3 in exactly the same way as the scatterometer observations.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00182", "doi": "10.48670/moi-00182", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-glo-phy-l3-nrt-012-002,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": "proprietary", "missionStartDate": "2016-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "WIND_GLO_PHY_L4_MY_012_006": {"abstract": "For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 and 0.25 degrees horizontal spatial resolution. Scatterometer observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF ERA5 model fields. Bias corrections are based on scatterometer observations from Metop-A, Metop-B, Metop-C ASCAT (0.125 degrees) and QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT (0.25 degrees). The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00185", "doi": "10.48670/moi-00185", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,global-ocean,level-4,marine-resources,marine-safety,multi-year,northward-wind,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence,wind-glo-phy-l4-my-012-006", "license": "proprietary", "missionStartDate": "1994-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_GLO_PHY_L4_NRT_012_004": {"abstract": "For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00305", "doi": "10.48670/moi-00305", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,global-ocean,level-4,marine-resources,marine-safety,near-real-time,northward-wind,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence,wind-glo-phy-l4-nrt-012-004", "license": "proprietary", "missionStartDate": "2020-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_MED_PHY_HR_L3_MY_012_109": {"abstract": "For the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00342", "doi": "10.48670/mds-00342", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-med-phy-hr-l3-my-012-109,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Mediterranean Sea"}, "WIND_MED_PHY_HR_L3_NRT_012_104": {"abstract": "For the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00334", "doi": "10.48670/mds-00334", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-med-phy-hr-l3-nrt-012-104,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Mediterranean Sea"}}, "providers_config": {"ANTARCTIC_OMI_SI_extent": {"collection": "ANTARCTIC_OMI_SI_extent"}, "ANTARCTIC_OMI_SI_extent_obs": {"collection": "ANTARCTIC_OMI_SI_extent_obs"}, "ARCTIC_ANALYSISFORECAST_BGC_002_004": {"collection": "ARCTIC_ANALYSISFORECAST_BGC_002_004"}, "ARCTIC_ANALYSISFORECAST_PHY_002_001": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_002_001"}, "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011"}, "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015"}, "ARCTIC_ANALYSIS_FORECAST_WAV_002_014": {"collection": "ARCTIC_ANALYSIS_FORECAST_WAV_002_014"}, "ARCTIC_MULTIYEAR_BGC_002_005": {"collection": "ARCTIC_MULTIYEAR_BGC_002_005"}, "ARCTIC_MULTIYEAR_PHY_002_003": {"collection": "ARCTIC_MULTIYEAR_PHY_002_003"}, "ARCTIC_MULTIYEAR_PHY_ICE_002_016": {"collection": "ARCTIC_MULTIYEAR_PHY_ICE_002_016"}, "ARCTIC_MULTIYEAR_WAV_002_013": {"collection": "ARCTIC_MULTIYEAR_WAV_002_013"}, "ARCTIC_OMI_SI_Transport_NordicSeas": {"collection": "ARCTIC_OMI_SI_Transport_NordicSeas"}, "ARCTIC_OMI_SI_extent": {"collection": "ARCTIC_OMI_SI_extent"}, "ARCTIC_OMI_SI_extent_obs": {"collection": "ARCTIC_OMI_SI_extent_obs"}, "ARCTIC_OMI_TEMPSAL_FWC": {"collection": "ARCTIC_OMI_TEMPSAL_FWC"}, "BALTICSEA_ANALYSISFORECAST_BGC_003_007": {"collection": "BALTICSEA_ANALYSISFORECAST_BGC_003_007"}, "BALTICSEA_ANALYSISFORECAST_PHY_003_006": {"collection": "BALTICSEA_ANALYSISFORECAST_PHY_003_006"}, "BALTICSEA_ANALYSISFORECAST_WAV_003_010": {"collection": "BALTICSEA_ANALYSISFORECAST_WAV_003_010"}, "BALTICSEA_MULTIYEAR_BGC_003_012": {"collection": "BALTICSEA_MULTIYEAR_BGC_003_012"}, "BALTICSEA_MULTIYEAR_PHY_003_011": {"collection": "BALTICSEA_MULTIYEAR_PHY_003_011"}, "BALTICSEA_MULTIYEAR_WAV_003_015": {"collection": "BALTICSEA_MULTIYEAR_WAV_003_015"}, "BALTICSEA_REANALYSIS_WAV_003_015": {"collection": "BALTICSEA_REANALYSIS_WAV_003_015"}, "BALTIC_OMI_HEALTH_codt_volume": {"collection": "BALTIC_OMI_HEALTH_codt_volume"}, "BALTIC_OMI_OHC_area_averaged_anomalies": {"collection": "BALTIC_OMI_OHC_area_averaged_anomalies"}, "BALTIC_OMI_SI_extent": {"collection": "BALTIC_OMI_SI_extent"}, "BALTIC_OMI_SI_volume": {"collection": "BALTIC_OMI_SI_volume"}, "BALTIC_OMI_TEMPSAL_Stz_trend": {"collection": "BALTIC_OMI_TEMPSAL_Stz_trend"}, "BALTIC_OMI_TEMPSAL_Ttz_trend": {"collection": "BALTIC_OMI_TEMPSAL_Ttz_trend"}, "BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm": {"collection": "BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm"}, "BALTIC_OMI_WMHE_mbi_sto2tz_gotland": {"collection": "BALTIC_OMI_WMHE_mbi_sto2tz_gotland"}, "BLKSEA_ANALYSISFORECAST_BGC_007_010": {"collection": "BLKSEA_ANALYSISFORECAST_BGC_007_010"}, "BLKSEA_ANALYSISFORECAST_PHY_007_001": {"collection": "BLKSEA_ANALYSISFORECAST_PHY_007_001"}, "BLKSEA_ANALYSISFORECAST_WAV_007_003": {"collection": "BLKSEA_ANALYSISFORECAST_WAV_007_003"}, "BLKSEA_MULTIYEAR_BGC_007_005": {"collection": "BLKSEA_MULTIYEAR_BGC_007_005"}, "BLKSEA_MULTIYEAR_PHY_007_004": {"collection": "BLKSEA_MULTIYEAR_PHY_007_004"}, "BLKSEA_MULTIYEAR_WAV_007_006": {"collection": "BLKSEA_MULTIYEAR_WAV_007_006"}, "BLKSEA_OMI_HEALTH_oxygen_trend": {"collection": "BLKSEA_OMI_HEALTH_oxygen_trend"}, "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "BLKSEA_OMI_TEMPSAL_sst_trend": {"collection": "BLKSEA_OMI_TEMPSAL_sst_trend"}, "GLOBAL_ANALYSISFORECAST_BGC_001_028": {"collection": "GLOBAL_ANALYSISFORECAST_BGC_001_028"}, "GLOBAL_ANALYSISFORECAST_PHY_001_024": {"collection": "GLOBAL_ANALYSISFORECAST_PHY_001_024"}, "GLOBAL_ANALYSISFORECAST_WAV_001_027": {"collection": "GLOBAL_ANALYSISFORECAST_WAV_001_027"}, "GLOBAL_MULTIYEAR_BGC_001_029": {"collection": "GLOBAL_MULTIYEAR_BGC_001_029"}, "GLOBAL_MULTIYEAR_BGC_001_033": {"collection": "GLOBAL_MULTIYEAR_BGC_001_033"}, "GLOBAL_MULTIYEAR_PHY_001_030": {"collection": "GLOBAL_MULTIYEAR_PHY_001_030"}, "GLOBAL_MULTIYEAR_PHY_ENS_001_031": {"collection": "GLOBAL_MULTIYEAR_PHY_ENS_001_031"}, "GLOBAL_MULTIYEAR_WAV_001_032": {"collection": "GLOBAL_MULTIYEAR_WAV_001_032"}, "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly": {"collection": "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly"}, "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies": {"collection": "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies"}, "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated": {"collection": "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated"}, "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged": {"collection": "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged"}, "GLOBAL_OMI_HEALTH_carbon_ph_trend": {"collection": "GLOBAL_OMI_HEALTH_carbon_ph_trend"}, "GLOBAL_OMI_NATLANTIC_amoc_26N_profile": {"collection": "GLOBAL_OMI_NATLANTIC_amoc_26N_profile"}, "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries": {"collection": "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000": {"collection": "GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_300": {"collection": "GLOBAL_OMI_OHC_area_averaged_anomalies_0_300"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_700": {"collection": "GLOBAL_OMI_OHC_area_averaged_anomalies_0_700"}, "GLOBAL_OMI_OHC_trend": {"collection": "GLOBAL_OMI_OHC_trend"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000": {"collection": "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700": {"collection": "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700"}, "GLOBAL_OMI_SL_thsl_trend": {"collection": "GLOBAL_OMI_SL_thsl_trend"}, "GLOBAL_OMI_TEMPSAL_Tyz_trend": {"collection": "GLOBAL_OMI_TEMPSAL_Tyz_trend"}, "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "GLOBAL_OMI_TEMPSAL_sst_trend": {"collection": "GLOBAL_OMI_TEMPSAL_sst_trend"}, "GLOBAL_OMI_WMHE_heattrp": {"collection": "GLOBAL_OMI_WMHE_heattrp"}, "GLOBAL_OMI_WMHE_northward_mht": {"collection": "GLOBAL_OMI_WMHE_northward_mht"}, "GLOBAL_OMI_WMHE_voltrp": {"collection": "GLOBAL_OMI_WMHE_voltrp"}, "IBI_ANALYSISFORECAST_BGC_005_004": {"collection": "IBI_ANALYSISFORECAST_BGC_005_004"}, "IBI_ANALYSISFORECAST_PHY_005_001": {"collection": "IBI_ANALYSISFORECAST_PHY_005_001"}, "IBI_ANALYSISFORECAST_WAV_005_005": {"collection": "IBI_ANALYSISFORECAST_WAV_005_005"}, "IBI_MULTIYEAR_BGC_005_003": {"collection": "IBI_MULTIYEAR_BGC_005_003"}, "IBI_MULTIYEAR_PHY_005_002": {"collection": "IBI_MULTIYEAR_PHY_005_002"}, "IBI_MULTIYEAR_WAV_005_006": {"collection": "IBI_MULTIYEAR_WAV_005_006"}, "IBI_OMI_CURRENTS_cui": {"collection": "IBI_OMI_CURRENTS_cui"}, "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "IBI_OMI_SEASTATE_swi": {"collection": "IBI_OMI_SEASTATE_swi"}, "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "IBI_OMI_WMHE_mow": {"collection": "IBI_OMI_WMHE_mow"}, "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031": {"collection": "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031"}, "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032": {"collection": "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032"}, "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034": {"collection": "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034"}, "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050": {"collection": "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050"}, "INSITU_GLO_BGC_DISCRETE_MY_013_046": {"collection": "INSITU_GLO_BGC_DISCRETE_MY_013_046"}, "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030": {"collection": "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030"}, "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053": {"collection": "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053"}, "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001": {"collection": "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001"}, "INSITU_GLO_PHY_TS_OA_MY_013_052": {"collection": "INSITU_GLO_PHY_TS_OA_MY_013_052"}, "INSITU_GLO_PHY_TS_OA_NRT_013_002": {"collection": "INSITU_GLO_PHY_TS_OA_NRT_013_002"}, "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044": {"collection": "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044"}, "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048": {"collection": "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048"}, "INSITU_GLO_WAV_DISCRETE_MY_013_045": {"collection": "INSITU_GLO_WAV_DISCRETE_MY_013_045"}, "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033": {"collection": "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033"}, "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035": {"collection": "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035"}, "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036": {"collection": "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036"}, "MEDSEA_ANALYSISFORECAST_BGC_006_014": {"collection": "MEDSEA_ANALYSISFORECAST_BGC_006_014"}, "MEDSEA_ANALYSISFORECAST_PHY_006_013": {"collection": "MEDSEA_ANALYSISFORECAST_PHY_006_013"}, "MEDSEA_ANALYSISFORECAST_WAV_006_017": {"collection": "MEDSEA_ANALYSISFORECAST_WAV_006_017"}, "MEDSEA_MULTIYEAR_BGC_006_008": {"collection": "MEDSEA_MULTIYEAR_BGC_006_008"}, "MEDSEA_MULTIYEAR_PHY_006_004": {"collection": "MEDSEA_MULTIYEAR_PHY_006_004"}, "MEDSEA_MULTIYEAR_WAV_006_012": {"collection": "MEDSEA_MULTIYEAR_WAV_006_012"}, "MEDSEA_OMI_OHC_area_averaged_anomalies": {"collection": "MEDSEA_OMI_OHC_area_averaged_anomalies"}, "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "MEDSEA_OMI_TEMPSAL_sst_trend": {"collection": "MEDSEA_OMI_TEMPSAL_sst_trend"}, "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009": {"collection": "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009"}, "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010": {"collection": "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008": {"collection": "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008": {"collection": "MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008"}, "MULTIOBS_GLO_PHY_MYNRT_015_003": {"collection": "MULTIOBS_GLO_PHY_MYNRT_015_003"}, "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014": {"collection": "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014"}, "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015": {"collection": "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015"}, "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013": {"collection": "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013"}, "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012": {"collection": "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012"}, "MULTIOBS_GLO_PHY_W_3D_REP_015_007": {"collection": "MULTIOBS_GLO_PHY_W_3D_REP_015_007"}, "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "NWSHELF_ANALYSISFORECAST_BGC_004_002": {"collection": "NWSHELF_ANALYSISFORECAST_BGC_004_002"}, "NWSHELF_ANALYSISFORECAST_PHY_004_013": {"collection": "NWSHELF_ANALYSISFORECAST_PHY_004_013"}, "NWSHELF_ANALYSISFORECAST_WAV_004_014": {"collection": "NWSHELF_ANALYSISFORECAST_WAV_004_014"}, "NWSHELF_MULTIYEAR_BGC_004_011": {"collection": "NWSHELF_MULTIYEAR_BGC_004_011"}, "NWSHELF_MULTIYEAR_PHY_004_009": {"collection": "NWSHELF_MULTIYEAR_PHY_004_009"}, "NWSHELF_REANALYSIS_WAV_004_015": {"collection": "NWSHELF_REANALYSIS_WAV_004_015"}, "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201": {"collection": "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201"}, "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207": {"collection": "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207"}, "OCEANCOLOUR_ARC_BGC_L3_MY_009_123": {"collection": "OCEANCOLOUR_ARC_BGC_L3_MY_009_123"}, "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121": {"collection": "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121"}, "OCEANCOLOUR_ARC_BGC_L4_MY_009_124": {"collection": "OCEANCOLOUR_ARC_BGC_L4_MY_009_124"}, "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122": {"collection": "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122"}, "OCEANCOLOUR_ATL_BGC_L3_MY_009_113": {"collection": "OCEANCOLOUR_ATL_BGC_L3_MY_009_113"}, "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111": {"collection": "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111"}, "OCEANCOLOUR_ATL_BGC_L4_MY_009_118": {"collection": "OCEANCOLOUR_ATL_BGC_L4_MY_009_118"}, "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116": {"collection": "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116"}, "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202": {"collection": "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202"}, "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208": {"collection": "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208"}, "OCEANCOLOUR_BAL_BGC_L3_MY_009_133": {"collection": "OCEANCOLOUR_BAL_BGC_L3_MY_009_133"}, "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131": {"collection": "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131"}, "OCEANCOLOUR_BAL_BGC_L4_MY_009_134": {"collection": "OCEANCOLOUR_BAL_BGC_L4_MY_009_134"}, "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132": {"collection": "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132"}, "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206": {"collection": "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206"}, "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212": {"collection": "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212"}, "OCEANCOLOUR_BLK_BGC_L3_MY_009_153": {"collection": "OCEANCOLOUR_BLK_BGC_L3_MY_009_153"}, "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151": {"collection": "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151"}, "OCEANCOLOUR_BLK_BGC_L4_MY_009_154": {"collection": "OCEANCOLOUR_BLK_BGC_L4_MY_009_154"}, "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152": {"collection": "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_103": {"collection": "OCEANCOLOUR_GLO_BGC_L3_MY_009_103"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_107": {"collection": "OCEANCOLOUR_GLO_BGC_L3_MY_009_107"}, "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101": {"collection": "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_104": {"collection": "OCEANCOLOUR_GLO_BGC_L4_MY_009_104"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_108": {"collection": "OCEANCOLOUR_GLO_BGC_L4_MY_009_108"}, "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102": {"collection": "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102"}, "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204": {"collection": "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204"}, "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210": {"collection": "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210"}, "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205": {"collection": "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205"}, "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211": {"collection": "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211"}, "OCEANCOLOUR_MED_BGC_L3_MY_009_143": {"collection": "OCEANCOLOUR_MED_BGC_L3_MY_009_143"}, "OCEANCOLOUR_MED_BGC_L3_NRT_009_141": {"collection": "OCEANCOLOUR_MED_BGC_L3_NRT_009_141"}, "OCEANCOLOUR_MED_BGC_L4_MY_009_144": {"collection": "OCEANCOLOUR_MED_BGC_L4_MY_009_144"}, "OCEANCOLOUR_MED_BGC_L4_NRT_009_142": {"collection": "OCEANCOLOUR_MED_BGC_L4_NRT_009_142"}, "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203": {"collection": "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203"}, "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209": {"collection": "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209"}, "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index": {"collection": "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index"}, "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged": {"collection": "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged"}, "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean": {"collection": "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean"}, "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean": {"collection": "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean"}, "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged": {"collection": "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged"}, "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies": {"collection": "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies"}, "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies"}, "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies"}, "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_OSC_MEDSEA_volume_mean": {"collection": "OMI_CLIMATE_OSC_MEDSEA_volume_mean"}, "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies"}, "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies"}, "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies"}, "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies"}, "OMI_CLIMATE_SL_GLOBAL_regional_trends": {"collection": "OMI_CLIMATE_SL_GLOBAL_regional_trends"}, "OMI_CLIMATE_SL_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies"}, "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies"}, "OMI_CLIMATE_SST_BAL_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_BAL_area_averaged_anomalies"}, "OMI_CLIMATE_SST_BAL_trend": {"collection": "OMI_CLIMATE_SST_BAL_trend"}, "OMI_CLIMATE_SST_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_SST_IBI_trend": {"collection": "OMI_CLIMATE_SST_IBI_trend"}, "OMI_CLIMATE_SST_IST_ARCTIC_anomaly": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_anomaly"}, "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies"}, "OMI_CLIMATE_SST_IST_ARCTIC_trend": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_trend"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_trend": {"collection": "OMI_CLIMATE_SST_NORTHWESTSHELF_trend"}, "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection": {"collection": "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection"}, "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies": {"collection": "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies"}, "OMI_EXTREME_SEASTATE_GLOBAL_swh_mean_and_P95_obs": {"collection": "OMI_EXTREME_SEASTATE_GLOBAL_swh_mean_and_P95_obs"}, "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_BLKSEA_recent_changes": {"collection": "OMI_EXTREME_WAVE_BLKSEA_recent_changes"}, "OMI_EXTREME_WAVE_BLKSEA_wave_power": {"collection": "OMI_EXTREME_WAVE_BLKSEA_wave_power"}, "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs"}, "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend"}, "OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean": {"collection": "OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean"}, "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012": {"collection": "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012"}, "SEAICE_ANT_PHY_L3_MY_011_018": {"collection": "SEAICE_ANT_PHY_L3_MY_011_018"}, "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023": {"collection": "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023"}, "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024": {"collection": "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024"}, "SEAICE_ARC_PHY_AUTO_L4_NRT_011_015": {"collection": "SEAICE_ARC_PHY_AUTO_L4_NRT_011_015"}, "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021": {"collection": "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021"}, "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016": {"collection": "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016"}, "SEAICE_ARC_PHY_L3M_NRT_011_017": {"collection": "SEAICE_ARC_PHY_L3M_NRT_011_017"}, "SEAICE_ARC_PHY_L4_NRT_011_014": {"collection": "SEAICE_ARC_PHY_L4_NRT_011_014"}, "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010": {"collection": "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008"}, "SEAICE_BAL_PHY_L4_MY_011_019": {"collection": "SEAICE_BAL_PHY_L4_MY_011_019"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004": {"collection": "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011": {"collection": "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011"}, "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013": {"collection": "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013"}, "SEAICE_GLO_PHY_L4_MY_011_020": {"collection": "SEAICE_GLO_PHY_L4_MY_011_020"}, "SEAICE_GLO_PHY_L4_NRT_011_014": {"collection": "SEAICE_GLO_PHY_L4_NRT_011_014"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001": {"collection": "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006": {"collection": "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006"}, "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009": {"collection": "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009"}, "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067": {"collection": "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067"}, "SEALEVEL_EUR_PHY_L3_MY_008_061": {"collection": "SEALEVEL_EUR_PHY_L3_MY_008_061"}, "SEALEVEL_EUR_PHY_L3_NRT_008_059": {"collection": "SEALEVEL_EUR_PHY_L3_NRT_008_059"}, "SEALEVEL_EUR_PHY_L4_MY_008_068": {"collection": "SEALEVEL_EUR_PHY_L4_MY_008_068"}, "SEALEVEL_EUR_PHY_L4_NRT_008_060": {"collection": "SEALEVEL_EUR_PHY_L4_NRT_008_060"}, "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070": {"collection": "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070"}, "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057": {"collection": "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057"}, "SEALEVEL_GLO_PHY_L3_MY_008_062": {"collection": "SEALEVEL_GLO_PHY_L3_MY_008_062"}, "SEALEVEL_GLO_PHY_L3_NRT_008_044": {"collection": "SEALEVEL_GLO_PHY_L3_NRT_008_044"}, "SEALEVEL_GLO_PHY_L4_MY_008_047": {"collection": "SEALEVEL_GLO_PHY_L4_MY_008_047"}, "SEALEVEL_GLO_PHY_L4_NRT_008_046": {"collection": "SEALEVEL_GLO_PHY_L4_NRT_008_046"}, "SEALEVEL_GLO_PHY_MDT_008_063": {"collection": "SEALEVEL_GLO_PHY_MDT_008_063"}, "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033": {"collection": "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033"}, "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066": {"collection": "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066"}, "SST_ATL_PHY_L3S_MY_010_038": {"collection": "SST_ATL_PHY_L3S_MY_010_038"}, "SST_ATL_PHY_L3S_NRT_010_037": {"collection": "SST_ATL_PHY_L3S_NRT_010_037"}, "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025": {"collection": "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025"}, "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026": {"collection": "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026"}, "SST_BAL_PHY_L3S_MY_010_040": {"collection": "SST_BAL_PHY_L3S_MY_010_040"}, "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034": {"collection": "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034"}, "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032": {"collection": "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032"}, "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b": {"collection": "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b"}, "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016": {"collection": "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016"}, "SST_BS_PHY_L3S_MY_010_041": {"collection": "SST_BS_PHY_L3S_MY_010_041"}, "SST_BS_PHY_SUBSKIN_L4_NRT_010_035": {"collection": "SST_BS_PHY_SUBSKIN_L4_NRT_010_035"}, "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013": {"collection": "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013"}, "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006": {"collection": "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006"}, "SST_BS_SST_L4_REP_OBSERVATIONS_010_022": {"collection": "SST_BS_SST_L4_REP_OBSERVATIONS_010_022"}, "SST_GLO_PHY_L3S_MY_010_039": {"collection": "SST_GLO_PHY_L3S_MY_010_039"}, "SST_GLO_PHY_L4_MY_010_044": {"collection": "SST_GLO_PHY_L4_MY_010_044"}, "SST_GLO_PHY_L4_NRT_010_043": {"collection": "SST_GLO_PHY_L4_NRT_010_043"}, "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010": {"collection": "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010"}, "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001": {"collection": "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011": {"collection": "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024": {"collection": "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024"}, "SST_MED_PHY_L3S_MY_010_042": {"collection": "SST_MED_PHY_L3S_MY_010_042"}, "SST_MED_PHY_SUBSKIN_L4_NRT_010_036": {"collection": "SST_MED_PHY_SUBSKIN_L4_NRT_010_036"}, "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012": {"collection": "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012"}, "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004": {"collection": "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004"}, "SST_MED_SST_L4_REP_OBSERVATIONS_010_021": {"collection": "SST_MED_SST_L4_REP_OBSERVATIONS_010_021"}, "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002": {"collection": "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002"}, "WAVE_GLO_PHY_SPC_L3_MY_014_006": {"collection": "WAVE_GLO_PHY_SPC_L3_MY_014_006"}, "WAVE_GLO_PHY_SPC_L3_NRT_014_009": {"collection": "WAVE_GLO_PHY_SPC_L3_NRT_014_009"}, "WAVE_GLO_PHY_SPC_L4_NRT_014_004": {"collection": "WAVE_GLO_PHY_SPC_L4_NRT_014_004"}, "WAVE_GLO_PHY_SWH_L3_MY_014_005": {"collection": "WAVE_GLO_PHY_SWH_L3_MY_014_005"}, "WAVE_GLO_PHY_SWH_L3_NRT_014_001": {"collection": "WAVE_GLO_PHY_SWH_L3_NRT_014_001"}, "WAVE_GLO_PHY_SWH_L4_MY_014_007": {"collection": "WAVE_GLO_PHY_SWH_L4_MY_014_007"}, "WAVE_GLO_PHY_SWH_L4_NRT_014_003": {"collection": "WAVE_GLO_PHY_SWH_L4_NRT_014_003"}, "WAVE_GLO_WAV_L3_SPC_NRT_OBSERVATIONS_014_002": {"collection": "WAVE_GLO_WAV_L3_SPC_NRT_OBSERVATIONS_014_002"}, "WIND_ARC_PHY_HR_L3_MY_012_105": {"collection": "WIND_ARC_PHY_HR_L3_MY_012_105"}, "WIND_ARC_PHY_HR_L3_NRT_012_100": {"collection": "WIND_ARC_PHY_HR_L3_NRT_012_100"}, "WIND_ATL_PHY_HR_L3_MY_012_106": {"collection": "WIND_ATL_PHY_HR_L3_MY_012_106"}, "WIND_ATL_PHY_HR_L3_NRT_012_101": {"collection": "WIND_ATL_PHY_HR_L3_NRT_012_101"}, "WIND_BAL_PHY_HR_L3_MY_012_107": {"collection": "WIND_BAL_PHY_HR_L3_MY_012_107"}, "WIND_BAL_PHY_HR_L3_NRT_012_102": {"collection": "WIND_BAL_PHY_HR_L3_NRT_012_102"}, "WIND_BLK_PHY_HR_L3_MY_012_108": {"collection": "WIND_BLK_PHY_HR_L3_MY_012_108"}, "WIND_BLK_PHY_HR_L3_NRT_012_103": {"collection": "WIND_BLK_PHY_HR_L3_NRT_012_103"}, "WIND_GLO_PHY_CLIMATE_L4_MY_012_003": {"collection": "WIND_GLO_PHY_CLIMATE_L4_MY_012_003"}, "WIND_GLO_PHY_L3_MY_012_005": {"collection": "WIND_GLO_PHY_L3_MY_012_005"}, "WIND_GLO_PHY_L3_NRT_012_002": {"collection": "WIND_GLO_PHY_L3_NRT_012_002"}, "WIND_GLO_PHY_L4_MY_012_006": {"collection": "WIND_GLO_PHY_L4_MY_012_006"}, "WIND_GLO_PHY_L4_NRT_012_004": {"collection": "WIND_GLO_PHY_L4_NRT_012_004"}, "WIND_MED_PHY_HR_L3_MY_012_109": {"collection": "WIND_MED_PHY_HR_L3_MY_012_109"}, "WIND_MED_PHY_HR_L3_NRT_012_104": {"collection": "WIND_MED_PHY_HR_L3_NRT_012_104"}}}, "earth_search": {"product_types_config": {"cop-dem-glo-30": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme.", "instrument": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-30"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-90 provides worldwide coverage at 90 meters.", "instrument": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-90"}, "landsat-c2-l2": {"abstract": "Atmospherically corrected global Landsat Collection 2 Level-2 data from the Thematic Mapper (TM) onboard Landsat 4 and 5, the Enhanced Thematic Mapper Plus (ETM+) onboard Landsat 7, and the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard Landsat 8 and 9.", "instrument": "tm,etm+,oli,tirs", "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "missionStartDate": "1982-08-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "title": "Landsat Collection 2 Level-2"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) provides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). NAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) within the [US Department of Agriculture](https://www.usda.gov/) (USDA). Data are captured at least once every three years for each state. This dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "missionStartDate": "2010-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NAIP: National Agriculture Imagery Program"}, "sentinel-1-grd": {"abstract": "Sentinel-1 is a pair of Synthetic Aperture Radar (SAR) imaging satellites launched in 2014 and 2016 by the European Space Agency (ESA). Their 6 day revisit cycle and ability to observe through clouds makes this dataset perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to cloud-optimized GeoTIFF format.", "instrument": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "missionStartDate": "2014-10-10T00:28:21Z", "platform": "sentinel-1", "platformSerialIdentifier": "sentinel-1a,sentinel-1b", "processingLevel": null, "title": "Sentinel-1 Level-1C Ground Range Detected (GRD)"}, "sentinel-2-c1-l2a": {"abstract": "Sentinel-2 Collection 1 Level-2A, data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-c1-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Collection 1 Level-2A"}, "sentinel-2-l1c": {"abstract": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-l1c,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Level-1C"}, "sentinel-2-l2a": {"abstract": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Level-2A"}, "sentinel-2-pre-c1-l2a": {"abstract": "Sentinel-2 Pre-Collection 1 Level-2A (baseline < 05.00), with data and metadata matching collection sentinel-2-c1-l2a", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-pre-c1-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Pre-Collection 1 Level-2A "}}, "providers_config": {"cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "naip": {"productType": "naip"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "sentinel-2-c1-l2a": {"productType": "sentinel-2-c1-l2a"}, "sentinel-2-l1c": {"productType": "sentinel-2-l1c"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "sentinel-2-pre-c1-l2a": {"productType": "sentinel-2-pre-c1-l2a"}}}, "eumetsat_ds": {"product_types_config": {"EO:EUM:CM:METOP:ASCSZFR02": {"abstract": null, "instrument": null, "keywords": "eo:eum:cm:metop:ascszfr02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:CM:METOP:ASCSZFR02"}, "EO:EUM:CM:METOP:ASCSZOR02": {"abstract": null, "instrument": null, "keywords": "eo:eum:cm:metop:ascszor02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:CM:METOP:ASCSZOR02"}, "EO:EUM:CM:METOP:ASCSZRR02": {"abstract": null, "instrument": null, "keywords": "eo:eum:cm:metop:ascszrr02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:CM:METOP:ASCSZRR02"}, "EO:EUM:DAT:0088": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0088", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0088"}, "EO:EUM:DAT:0142": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0142", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0142"}, "EO:EUM:DAT:0143": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0143", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0143"}, "EO:EUM:DAT:0236": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0236", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0236"}, "EO:EUM:DAT:0237": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0237", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0237"}, "EO:EUM:DAT:0238": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0238", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0238"}, "EO:EUM:DAT:0239": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0239", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0239"}, "EO:EUM:DAT:0240": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0240", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0240"}, "EO:EUM:DAT:0241": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0241", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0241"}, "EO:EUM:DAT:0274": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0274", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0274"}, "EO:EUM:DAT:0300": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0300", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0300"}, "EO:EUM:DAT:0301": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0301", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0301"}, "EO:EUM:DAT:0302": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0302", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0302"}, "EO:EUM:DAT:0303": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0303", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0303"}, "EO:EUM:DAT:0305": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0305", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0305"}, "EO:EUM:DAT:0343": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0343", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0343"}, "EO:EUM:DAT:0344": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0344", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0344"}, "EO:EUM:DAT:0345": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0345", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0345"}, "EO:EUM:DAT:0348": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0348", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0348"}, "EO:EUM:DAT:0349": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0349", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0349"}, "EO:EUM:DAT:0374": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0374", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0374"}, "EO:EUM:DAT:0394": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0394", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0394"}, "EO:EUM:DAT:0398": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0398", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0398"}, "EO:EUM:DAT:0405": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0405", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0405"}, "EO:EUM:DAT:0406": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0406", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0406"}, "EO:EUM:DAT:0407": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0407", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0407"}, "EO:EUM:DAT:0408": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0408", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0408"}, "EO:EUM:DAT:0409": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0409", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0409"}, "EO:EUM:DAT:0410": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0410", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0410"}, "EO:EUM:DAT:0411": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0411", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0411"}, "EO:EUM:DAT:0412": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0412", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0412"}, "EO:EUM:DAT:0413": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0413", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0413"}, "EO:EUM:DAT:0414": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0414", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0414"}, "EO:EUM:DAT:0415": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0415", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0415"}, "EO:EUM:DAT:0416": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0416", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0416"}, "EO:EUM:DAT:0417": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0417", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0417"}, "EO:EUM:DAT:0533": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0533", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0533"}, "EO:EUM:DAT:0556": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0556", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0556"}, "EO:EUM:DAT:0557": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0557", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0557"}, "EO:EUM:DAT:0558": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0558", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0558"}, "EO:EUM:DAT:0576": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0576", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0576"}, "EO:EUM:DAT:0577": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0577", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0577"}, "EO:EUM:DAT:0578": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0578", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0578"}, "EO:EUM:DAT:0579": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0579", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0579"}, "EO:EUM:DAT:0581": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0581", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0581"}, "EO:EUM:DAT:0582": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0582", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0582"}, "EO:EUM:DAT:0583": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0583", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0583"}, "EO:EUM:DAT:0584": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0584", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0584"}, "EO:EUM:DAT:0585": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0585", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0585"}, "EO:EUM:DAT:0586": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0586", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0586"}, "EO:EUM:DAT:0601": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0601", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0601"}, "EO:EUM:DAT:0615": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0615", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0615"}, "EO:EUM:DAT:0617": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0617", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0617"}, "EO:EUM:DAT:0645": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0645", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0645"}, "EO:EUM:DAT:0647": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0647", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0647"}, "EO:EUM:DAT:0662": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0662", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0662"}, "EO:EUM:DAT:0665": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0665", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0665"}, "EO:EUM:DAT:0686": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0686", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0686"}, "EO:EUM:DAT:0687": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0687", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0687"}, "EO:EUM:DAT:0688": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0688", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0688"}, "EO:EUM:DAT:0690": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0690", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0690"}, "EO:EUM:DAT:0691": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0691", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0691"}, "EO:EUM:DAT:0758": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0758", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0758"}, "EO:EUM:DAT:0782": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0782", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0782"}, "EO:EUM:DAT:0833": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0833", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0833"}, "EO:EUM:DAT:0834": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0834", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0834"}, "EO:EUM:DAT:0835": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0835", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0835"}, "EO:EUM:DAT:0836": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0836", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0836"}, "EO:EUM:DAT:0837": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0837", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0837"}, "EO:EUM:DAT:0838": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0838", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0838"}, "EO:EUM:DAT:0839": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0839", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0839"}, "EO:EUM:DAT:0840": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0840", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0840"}, "EO:EUM:DAT:0841": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0841", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0841"}, "EO:EUM:DAT:0842": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0842", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0842"}, "EO:EUM:DAT:0850": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0850", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0850"}, "EO:EUM:DAT:0851": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0851", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0851"}, "EO:EUM:DAT:0852": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0852", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0852"}, "EO:EUM:DAT:0853": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0853", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0853"}, "EO:EUM:DAT:0854": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0854", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0854"}, "EO:EUM:DAT:0855": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0855", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0855"}, "EO:EUM:DAT:0856": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0856", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0856"}, "EO:EUM:DAT:0857": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0857", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0857"}, "EO:EUM:DAT:0858": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0858", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0858"}, "EO:EUM:DAT:0859": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0859", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0859"}, "EO:EUM:DAT:0862": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0862", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0862"}, "EO:EUM:DAT:0880": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0880", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0880"}, "EO:EUM:DAT:0881": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0881", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0881"}, "EO:EUM:DAT:0882": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0882", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0882"}, "EO:EUM:DAT:0894": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0894", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0894"}, "EO:EUM:DAT:0895": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0895", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0895"}, "EO:EUM:DAT:0959": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0959", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0959"}, "EO:EUM:DAT:0960": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0960", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0960"}, "EO:EUM:DAT:0961": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0961", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0961"}, "EO:EUM:DAT:0963": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0963", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0963"}, "EO:EUM:DAT:0964": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0964", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0964"}, "EO:EUM:DAT:DMSP:OSI-401-B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:dmsp:osi-401-b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:DMSP:OSI-401-B"}, "EO:EUM:DAT:METOP:AMSUL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:amsul1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:AMSUL1"}, "EO:EUM:DAT:METOP:ASCSZF1B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:ascszf1b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:ASCSZF1B"}, "EO:EUM:DAT:METOP:ASCSZO1B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:ascszo1b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:ASCSZO1B"}, "EO:EUM:DAT:METOP:ASCSZR1B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:ascszr1b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:ASCSZR1B"}, "EO:EUM:DAT:METOP:AVHRRL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:avhrrl1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:AVHRRL1"}, "EO:EUM:DAT:METOP:GLB-SST-NC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:glb-sst-nc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:GLB-SST-NC"}, "EO:EUM:DAT:METOP:GOMEL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:gomel1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:GOMEL1"}, "EO:EUM:DAT:METOP:IASIL1C-ALL": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:iasil1c-all", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:IASIL1C-ALL"}, "EO:EUM:DAT:METOP:IASIL2COX": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:iasil2cox", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:IASIL2COX"}, "EO:EUM:DAT:METOP:IASSND02": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:iassnd02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:IASSND02"}, "EO:EUM:DAT:METOP:LSA-002": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:lsa-002", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:LSA-002"}, "EO:EUM:DAT:METOP:MHSL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:mhsl1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:MHSL1"}, "EO:EUM:DAT:METOP:NTO": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:nto", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:NTO"}, "EO:EUM:DAT:METOP:OAS025": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:oas025", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OAS025"}, "EO:EUM:DAT:METOP:OSI-104": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:osi-104", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OSI-104"}, "EO:EUM:DAT:METOP:OSI-150-A": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:osi-150-a", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OSI-150-A"}, "EO:EUM:DAT:METOP:OSI-150-B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:osi-150-b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OSI-150-B"}, "EO:EUM:DAT:METOP:SOMO12": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:somo12", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:SOMO12"}, "EO:EUM:DAT:METOP:SOMO25": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:somo25", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:SOMO25"}, "EO:EUM:DAT:MSG:CLM": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:clm", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:CLM"}, "EO:EUM:DAT:MSG:CLM-IODC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:clm-iodc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:CLM-IODC"}, "EO:EUM:DAT:MSG:HRSEVIRI": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:hrseviri", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:HRSEVIRI"}, "EO:EUM:DAT:MSG:HRSEVIRI-IODC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:hrseviri-iodc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:HRSEVIRI-IODC"}, "EO:EUM:DAT:MSG:MSG15-RSS": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:msg15-rss", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:MSG15-RSS"}, "EO:EUM:DAT:MSG:RSS-CLM": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:rss-clm", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:RSS-CLM"}, "EO:EUM:DAT:MULT:HIRSL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:mult:hirsl1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MULT:HIRSL1"}}, "providers_config": {"EO:EUM:CM:METOP:ASCSZFR02": {"parentIdentifier": "EO:EUM:CM:METOP:ASCSZFR02"}, "EO:EUM:CM:METOP:ASCSZOR02": {"parentIdentifier": "EO:EUM:CM:METOP:ASCSZOR02"}, "EO:EUM:CM:METOP:ASCSZRR02": {"parentIdentifier": "EO:EUM:CM:METOP:ASCSZRR02"}, "EO:EUM:DAT:0088": {"parentIdentifier": "EO:EUM:DAT:0088"}, "EO:EUM:DAT:0142": {"parentIdentifier": "EO:EUM:DAT:0142"}, "EO:EUM:DAT:0143": {"parentIdentifier": "EO:EUM:DAT:0143"}, "EO:EUM:DAT:0236": {"parentIdentifier": "EO:EUM:DAT:0236"}, "EO:EUM:DAT:0237": {"parentIdentifier": "EO:EUM:DAT:0237"}, "EO:EUM:DAT:0238": {"parentIdentifier": "EO:EUM:DAT:0238"}, "EO:EUM:DAT:0239": {"parentIdentifier": "EO:EUM:DAT:0239"}, "EO:EUM:DAT:0240": {"parentIdentifier": "EO:EUM:DAT:0240"}, "EO:EUM:DAT:0241": {"parentIdentifier": "EO:EUM:DAT:0241"}, "EO:EUM:DAT:0274": {"parentIdentifier": "EO:EUM:DAT:0274"}, "EO:EUM:DAT:0300": {"parentIdentifier": "EO:EUM:DAT:0300"}, "EO:EUM:DAT:0301": {"parentIdentifier": "EO:EUM:DAT:0301"}, "EO:EUM:DAT:0302": {"parentIdentifier": "EO:EUM:DAT:0302"}, "EO:EUM:DAT:0303": {"parentIdentifier": "EO:EUM:DAT:0303"}, "EO:EUM:DAT:0305": {"parentIdentifier": "EO:EUM:DAT:0305"}, "EO:EUM:DAT:0343": {"parentIdentifier": "EO:EUM:DAT:0343"}, "EO:EUM:DAT:0344": {"parentIdentifier": "EO:EUM:DAT:0344"}, "EO:EUM:DAT:0345": {"parentIdentifier": "EO:EUM:DAT:0345"}, "EO:EUM:DAT:0348": {"parentIdentifier": "EO:EUM:DAT:0348"}, "EO:EUM:DAT:0349": {"parentIdentifier": "EO:EUM:DAT:0349"}, "EO:EUM:DAT:0374": {"parentIdentifier": "EO:EUM:DAT:0374"}, "EO:EUM:DAT:0394": {"parentIdentifier": "EO:EUM:DAT:0394"}, "EO:EUM:DAT:0398": {"parentIdentifier": "EO:EUM:DAT:0398"}, "EO:EUM:DAT:0405": {"parentIdentifier": "EO:EUM:DAT:0405"}, "EO:EUM:DAT:0406": {"parentIdentifier": "EO:EUM:DAT:0406"}, "EO:EUM:DAT:0407": {"parentIdentifier": "EO:EUM:DAT:0407"}, "EO:EUM:DAT:0408": {"parentIdentifier": "EO:EUM:DAT:0408"}, "EO:EUM:DAT:0409": {"parentIdentifier": "EO:EUM:DAT:0409"}, "EO:EUM:DAT:0410": {"parentIdentifier": "EO:EUM:DAT:0410"}, "EO:EUM:DAT:0411": {"parentIdentifier": "EO:EUM:DAT:0411"}, "EO:EUM:DAT:0412": {"parentIdentifier": "EO:EUM:DAT:0412"}, "EO:EUM:DAT:0413": {"parentIdentifier": "EO:EUM:DAT:0413"}, "EO:EUM:DAT:0414": {"parentIdentifier": "EO:EUM:DAT:0414"}, "EO:EUM:DAT:0415": {"parentIdentifier": "EO:EUM:DAT:0415"}, "EO:EUM:DAT:0416": {"parentIdentifier": "EO:EUM:DAT:0416"}, "EO:EUM:DAT:0417": {"parentIdentifier": "EO:EUM:DAT:0417"}, "EO:EUM:DAT:0533": {"parentIdentifier": "EO:EUM:DAT:0533"}, "EO:EUM:DAT:0556": {"parentIdentifier": "EO:EUM:DAT:0556"}, "EO:EUM:DAT:0557": {"parentIdentifier": "EO:EUM:DAT:0557"}, "EO:EUM:DAT:0558": {"parentIdentifier": "EO:EUM:DAT:0558"}, "EO:EUM:DAT:0576": {"parentIdentifier": "EO:EUM:DAT:0576"}, "EO:EUM:DAT:0577": {"parentIdentifier": "EO:EUM:DAT:0577"}, "EO:EUM:DAT:0578": {"parentIdentifier": "EO:EUM:DAT:0578"}, "EO:EUM:DAT:0579": {"parentIdentifier": "EO:EUM:DAT:0579"}, "EO:EUM:DAT:0581": {"parentIdentifier": "EO:EUM:DAT:0581"}, "EO:EUM:DAT:0582": {"parentIdentifier": "EO:EUM:DAT:0582"}, "EO:EUM:DAT:0583": {"parentIdentifier": "EO:EUM:DAT:0583"}, "EO:EUM:DAT:0584": {"parentIdentifier": "EO:EUM:DAT:0584"}, "EO:EUM:DAT:0585": {"parentIdentifier": "EO:EUM:DAT:0585"}, "EO:EUM:DAT:0586": {"parentIdentifier": "EO:EUM:DAT:0586"}, "EO:EUM:DAT:0601": {"parentIdentifier": "EO:EUM:DAT:0601"}, "EO:EUM:DAT:0615": {"parentIdentifier": "EO:EUM:DAT:0615"}, "EO:EUM:DAT:0617": {"parentIdentifier": "EO:EUM:DAT:0617"}, "EO:EUM:DAT:0645": {"parentIdentifier": "EO:EUM:DAT:0645"}, "EO:EUM:DAT:0647": {"parentIdentifier": "EO:EUM:DAT:0647"}, "EO:EUM:DAT:0662": {"parentIdentifier": "EO:EUM:DAT:0662"}, "EO:EUM:DAT:0665": {"parentIdentifier": "EO:EUM:DAT:0665"}, "EO:EUM:DAT:0686": {"parentIdentifier": "EO:EUM:DAT:0686"}, "EO:EUM:DAT:0687": {"parentIdentifier": "EO:EUM:DAT:0687"}, "EO:EUM:DAT:0688": {"parentIdentifier": "EO:EUM:DAT:0688"}, "EO:EUM:DAT:0690": {"parentIdentifier": "EO:EUM:DAT:0690"}, "EO:EUM:DAT:0691": {"parentIdentifier": "EO:EUM:DAT:0691"}, "EO:EUM:DAT:0758": {"parentIdentifier": "EO:EUM:DAT:0758"}, "EO:EUM:DAT:0782": {"parentIdentifier": "EO:EUM:DAT:0782"}, "EO:EUM:DAT:0833": {"parentIdentifier": "EO:EUM:DAT:0833"}, "EO:EUM:DAT:0834": {"parentIdentifier": "EO:EUM:DAT:0834"}, "EO:EUM:DAT:0835": {"parentIdentifier": "EO:EUM:DAT:0835"}, "EO:EUM:DAT:0836": {"parentIdentifier": "EO:EUM:DAT:0836"}, "EO:EUM:DAT:0837": {"parentIdentifier": "EO:EUM:DAT:0837"}, "EO:EUM:DAT:0838": {"parentIdentifier": "EO:EUM:DAT:0838"}, "EO:EUM:DAT:0839": {"parentIdentifier": "EO:EUM:DAT:0839"}, "EO:EUM:DAT:0840": {"parentIdentifier": "EO:EUM:DAT:0840"}, "EO:EUM:DAT:0841": {"parentIdentifier": "EO:EUM:DAT:0841"}, "EO:EUM:DAT:0842": {"parentIdentifier": "EO:EUM:DAT:0842"}, "EO:EUM:DAT:0850": {"parentIdentifier": "EO:EUM:DAT:0850"}, "EO:EUM:DAT:0851": {"parentIdentifier": "EO:EUM:DAT:0851"}, "EO:EUM:DAT:0852": {"parentIdentifier": "EO:EUM:DAT:0852"}, "EO:EUM:DAT:0853": {"parentIdentifier": "EO:EUM:DAT:0853"}, "EO:EUM:DAT:0854": {"parentIdentifier": "EO:EUM:DAT:0854"}, "EO:EUM:DAT:0855": {"parentIdentifier": "EO:EUM:DAT:0855"}, "EO:EUM:DAT:0856": {"parentIdentifier": "EO:EUM:DAT:0856"}, "EO:EUM:DAT:0857": {"parentIdentifier": "EO:EUM:DAT:0857"}, "EO:EUM:DAT:0858": {"parentIdentifier": "EO:EUM:DAT:0858"}, "EO:EUM:DAT:0859": {"parentIdentifier": "EO:EUM:DAT:0859"}, "EO:EUM:DAT:0862": {"parentIdentifier": "EO:EUM:DAT:0862"}, "EO:EUM:DAT:0880": {"parentIdentifier": "EO:EUM:DAT:0880"}, "EO:EUM:DAT:0881": {"parentIdentifier": "EO:EUM:DAT:0881"}, "EO:EUM:DAT:0882": {"parentIdentifier": "EO:EUM:DAT:0882"}, "EO:EUM:DAT:0894": {"parentIdentifier": "EO:EUM:DAT:0894"}, "EO:EUM:DAT:0895": {"parentIdentifier": "EO:EUM:DAT:0895"}, "EO:EUM:DAT:0959": {"parentIdentifier": "EO:EUM:DAT:0959"}, "EO:EUM:DAT:0960": {"parentIdentifier": "EO:EUM:DAT:0960"}, "EO:EUM:DAT:0961": {"parentIdentifier": "EO:EUM:DAT:0961"}, "EO:EUM:DAT:0963": {"parentIdentifier": "EO:EUM:DAT:0963"}, "EO:EUM:DAT:0964": {"parentIdentifier": "EO:EUM:DAT:0964"}, "EO:EUM:DAT:DMSP:OSI-401-B": {"parentIdentifier": "EO:EUM:DAT:DMSP:OSI-401-B"}, "EO:EUM:DAT:METOP:AMSUL1": {"parentIdentifier": "EO:EUM:DAT:METOP:AMSUL1"}, "EO:EUM:DAT:METOP:ASCSZF1B": {"parentIdentifier": "EO:EUM:DAT:METOP:ASCSZF1B"}, "EO:EUM:DAT:METOP:ASCSZO1B": {"parentIdentifier": "EO:EUM:DAT:METOP:ASCSZO1B"}, "EO:EUM:DAT:METOP:ASCSZR1B": {"parentIdentifier": "EO:EUM:DAT:METOP:ASCSZR1B"}, "EO:EUM:DAT:METOP:AVHRRL1": {"parentIdentifier": "EO:EUM:DAT:METOP:AVHRRL1"}, "EO:EUM:DAT:METOP:GLB-SST-NC": {"parentIdentifier": "EO:EUM:DAT:METOP:GLB-SST-NC"}, "EO:EUM:DAT:METOP:GOMEL1": {"parentIdentifier": "EO:EUM:DAT:METOP:GOMEL1"}, "EO:EUM:DAT:METOP:IASIL1C-ALL": {"parentIdentifier": "EO:EUM:DAT:METOP:IASIL1C-ALL"}, "EO:EUM:DAT:METOP:IASIL2COX": {"parentIdentifier": "EO:EUM:DAT:METOP:IASIL2COX"}, "EO:EUM:DAT:METOP:IASSND02": {"parentIdentifier": "EO:EUM:DAT:METOP:IASSND02"}, "EO:EUM:DAT:METOP:LSA-002": {"parentIdentifier": "EO:EUM:DAT:METOP:LSA-002"}, "EO:EUM:DAT:METOP:MHSL1": {"parentIdentifier": "EO:EUM:DAT:METOP:MHSL1"}, "EO:EUM:DAT:METOP:NTO": {"parentIdentifier": "EO:EUM:DAT:METOP:NTO"}, "EO:EUM:DAT:METOP:OAS025": {"parentIdentifier": "EO:EUM:DAT:METOP:OAS025"}, "EO:EUM:DAT:METOP:OSI-104": {"parentIdentifier": "EO:EUM:DAT:METOP:OSI-104"}, "EO:EUM:DAT:METOP:OSI-150-A": {"parentIdentifier": "EO:EUM:DAT:METOP:OSI-150-A"}, "EO:EUM:DAT:METOP:OSI-150-B": {"parentIdentifier": "EO:EUM:DAT:METOP:OSI-150-B"}, "EO:EUM:DAT:METOP:SOMO12": {"parentIdentifier": "EO:EUM:DAT:METOP:SOMO12"}, "EO:EUM:DAT:METOP:SOMO25": {"parentIdentifier": "EO:EUM:DAT:METOP:SOMO25"}, "EO:EUM:DAT:MSG:CLM": {"parentIdentifier": "EO:EUM:DAT:MSG:CLM"}, "EO:EUM:DAT:MSG:CLM-IODC": {"parentIdentifier": "EO:EUM:DAT:MSG:CLM-IODC"}, "EO:EUM:DAT:MSG:HRSEVIRI": {"parentIdentifier": "EO:EUM:DAT:MSG:HRSEVIRI"}, "EO:EUM:DAT:MSG:HRSEVIRI-IODC": {"parentIdentifier": "EO:EUM:DAT:MSG:HRSEVIRI-IODC"}, "EO:EUM:DAT:MSG:MSG15-RSS": {"parentIdentifier": "EO:EUM:DAT:MSG:MSG15-RSS"}, "EO:EUM:DAT:MSG:RSS-CLM": {"parentIdentifier": "EO:EUM:DAT:MSG:RSS-CLM"}, "EO:EUM:DAT:MULT:HIRSL1": {"parentIdentifier": "EO:EUM:DAT:MULT:HIRSL1"}}}, "geodes": null, "planetary_computer": {"product_types_config": {"3dep-lidar-classification": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm). See [LAS specification](https://www.ogc.org/standards/LAS) for details.\n\nThis COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html) to select a subset of interesting classifications. Do note that not all LiDAR collections contain a full compliment of classification labels.\nTo remove outliers, the PDAL pipeline uses a noise filter and then outputs the Classification dimension.\n\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets) and [`classification`](https://github.com/stac-extensions/classification) extensions. These classes are displayed in the \"Item assets\" below. You can programmatically access the full list of class values and descriptions using the `classification:classes` field form the `data` asset on the STAC collection.\n\nClassification rasters were produced as a subset of LiDAR classification categories:\n\n```\n0, Never Classified\n1, Unclassified\n2, Ground\n3, Low Vegetation\n4, Medium Vegetation\n5, High Vegetation\n6, Building\n9, Water\n10, Rail\n11, Road\n17, Bridge Deck\n```\n", "instrument": null, "keywords": "3dep,3dep-lidar-classification,classification,cog,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Classification"}, "3dep-lidar-copc": {"abstract": "This collection contains source data from the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program) reformatted into the [COPC](https://copc.io) format. A COPC file is a LAZ 1.4 file that stores point data organized in a clustered octree. It contains a VLR that describes the octree organization of data that are stored in LAZ 1.4 chunks. The end product is a one-to-one mapping of LAZ to UTM-reprojected COPC files.\n\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud) (LPC) content stored in the compressed [LASzip](https://laszip.org?) format. Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io) organization for use in Planetary Computer, which supports incremental spatial access and cloud streaming.\n\nLPC can be summarized for construction of digital terrain models (DTM), filtered for extraction of features like vegetation and buildings, and visualized to provide a point cloud map of the physical spaces the laser scanner interacted with. LPC content from 3DEP is used to compute and extract a variety of landscape characterization products, and some of them are provided by Planetary Computer, including Height Above Ground, Relative Intensity Image, and DTM and Digital Surface Models.\n\nThe LAZ tiles represent a one-to-one mapping of original tiled content as provided by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with the exception that the data were reprojected and normalized into appropriate UTM zones for their location without adjustment to the vertical datum. In some cases, vertical datum description may not match actual data values, especially for pre-2010 USGS 3DEP point cloud data.\n\nIn addition to these COPC files, various higher-level derived products are available as Cloud Optimized GeoTIFFs in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).", "instrument": null, "keywords": "3dep,3dep-lidar-copc,cog,point-cloud,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Point Cloud"}, "3dep-lidar-dsm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "keywords": "3dep,3dep-lidar-dsm,cog,dsm,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Digital Surface Model"}, "3dep-lidar-dtm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) to output a collection of Cloud Optimized GeoTIFFs.\n\nThe Simple Morphological Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).", "instrument": null, "keywords": "3dep,3dep-lidar-dtm,cog,dtm,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Digital Terrain Model"}, "3dep-lidar-dtm-native": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using the vendor provided (native) ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "keywords": "3dep,3dep-lidar-dtm-native,cog,dtm,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Digital Terrain Model (Native)"}, "3dep-lidar-hag": {"abstract": "This COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc) data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\n\nThe Height Above Ground Nearest Neighbor filter takes as input a point cloud with Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround, that contains the normalized height values.\n\nGround points may be generated with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf) or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf), but you can use any method you choose, as long as the ground returns are marked.\n\nNormalized heights are a commonly used attribute of point cloud data. This can also be referred to as height above ground (HAG) or above ground level (AGL) heights. In the end, it is simply a measure of a point's relative height as opposed to its raw elevation value.\n\nThe filter finds the number of ground points nearest to the non-ground point under consideration. It calculates an average ground height weighted by the distance of each ground point from the non-ground point. The HeightAboveGround is the difference between the Z value of the non-ground point and the interpolated ground height.\n", "instrument": null, "keywords": "3dep,3dep-lidar-hag,cog,elevation,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Height above Ground"}, "3dep-lidar-intensity": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the pulse return magnitude.\n\nThe values are based on the Intensity [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "keywords": "3dep,3dep-lidar-intensity,cog,intensity,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Intensity"}, "3dep-lidar-pointsourceid": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the file source ID from which the point originated. Zero indicates that the point originated in the current file.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "keywords": "3dep,3dep-lidar-pointsourceid,cog,pointsourceid,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Point Source"}, "3dep-lidar-returns": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the number of returns for a given pulse.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\n\nThe values are based on the NumberOfReturns [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "keywords": "3dep,3dep-lidar-returns,cog,numberofreturns,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Returns"}, "3dep-seamless": {"abstract": "U.S.-wide digital elevation data at horizontal resolutions ranging from one to sixty meters.\n\nThe [USGS 3D Elevation Program (3DEP) Datasets](https://www.usgs.gov/core-science-systems/ngp/3dep) from the [National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map) are the primary elevation data product produced and distributed by the USGS. The 3DEP program provides raster elevation data for the conterminous United States, Alaska, Hawaii, and the island territories, at a variety of spatial resolutions. The seamless DEM layers produced by the 3DEP program are updated frequently to integrate newly available, improved elevation source data. \n\nDEM layers are available nationally at grid spacings of 1 arc-second (approximately 30 meters) for the conterminous United States, and at approximately 1, 3, and 9 meters for parts of the United States. Most seamless DEM data for Alaska is available at a resolution of approximately 60 meters, where only lower resolution source data exist.\n", "instrument": null, "keywords": "3dep,3dep-seamless,dem,elevation,ned,usgs", "license": "PDDL-1.0", "missionStartDate": "1925-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Seamless DEMs"}, "alos-dem": {"abstract": "The \"ALOS World 3D-30m\" (AW3D30) dataset is a 30 meter resolution global digital surface model (DSM), developed by the Japan Aerospace Exploration Agency (JAXA). AWD30 was constructed from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) on board Advanced Land Observing Satellite (ALOS), operated from 2006 to 2011.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v3.2_product_e_e1.2.pdf) for more details.\n", "instrument": "prism", "keywords": "alos,alos-dem,dem,dsm,elevation,jaxa,prism", "license": "proprietary", "missionStartDate": "2016-12-07T00:00:00Z", "platform": null, "platformSerialIdentifier": "alos", "processingLevel": null, "title": "ALOS World 3D-30m"}, "alos-fnf-mosaic": {"abstract": "The global 25m resolution SAR mosaics and forest/non-forest maps are free and open annual datasets generated by [JAXA](https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm) using the L-band Synthetic Aperture Radar sensors on the Advanced Land Observing Satellite-2 (ALOS-2 PALSAR-2), the Advanced Land Observing Satellite (ALOS PALSAR) and the Japanese Earth Resources Satellite-1 (JERS-1 SAR).\n\nThe global forest/non-forest maps (FNF) were generated by a Random Forest machine learning-based classification method, with the re-processed global 25m resolution [PALSAR-2 mosaic dataset](https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic) (Ver. 2.0.0) as input. Here, the \"forest\" is defined as the tree covered land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance with the FAO definition of forest. The classification results are presented in four categories, with two categories of forest areas: forests with a canopy cover of 90 % or more and forests with a canopy cover of 10 % to 90 %, depending on the density of the forest area.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf) for more details.\n", "instrument": "PALSAR,PALSAR-2", "keywords": "alos,alos-2,alos-fnf-mosaic,forest,global,jaxa,land-cover,palsar,palsar-2", "license": "proprietary", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "title": "ALOS Forest/Non-Forest Annual Mosaic"}, "alos-palsar-mosaic": {"abstract": "Global 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)", "instrument": "PALSAR,PALSAR-2", "keywords": "alos,alos-2,alos-palsar-mosaic,global,jaxa,palsar,palsar-2,remote-sensing", "license": "proprietary", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "title": "ALOS PALSAR Annual Mosaic"}, "aster-l1t": {"abstract": "The [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument, launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution. ASTER images provide information about land surface temperature, color, elevation, and mineral composition.\n\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/) data from 2000-2006. L1T images have been terrain-corrected and rotated to a north-up UTM projection. Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "aster", "keywords": "aster,aster-l1t,global,nasa,satellite,terra,usgs", "license": "proprietary", "missionStartDate": "2000-03-04T12:00:00Z", "platform": null, "platformSerialIdentifier": "terra", "processingLevel": null, "title": "ASTER L1T"}, "chesapeake-lc-13": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of 13 land cover classes, although not all classes are used in all areas. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf) and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-13,land-cover", "license": "proprietary", "missionStartDate": "2013-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chesapeake Land Cover (13-class)"}, "chesapeake-lc-7": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of a uniform set of 7 land cover classes. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf). Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-7,land-cover", "license": "proprietary", "missionStartDate": "2013-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chesapeake Land Cover (7-class)"}, "chesapeake-lu": {"abstract": "A high-resolution 1-meter [land use data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-use-data-project/) in raster format for the entire Chesapeake Bay watershed. The dataset was created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) using 13 ancillary datasets including data on zoning, land use, parcel boundaries, landfills, floodplains, and wetlands. The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions that leads and directs Chesapeake Bay restoration efforts.\n\nThe dataset is composed of 17 land use classes in Virginia and 16 classes in all other jurisdictions. Additional information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lu,land-use", "license": "proprietary", "missionStartDate": "2013-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chesapeake Land Use"}, "chloris-biomass": {"abstract": "The Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems. It covers the period 2003 - 2019, at annual time steps. The global dataset has a circa 4.6 km spatial resolution.\n\nThe maps and data sets were generated by combining multiple remote sensing measurements from space borne satellites, processed using state-of-the-art machine learning and statistical methods, validated with field data from multiple countries. The dataset provides direct estimates of aboveground stock and change, and are not based on land use or land cover area change, and as such they include gains and losses of carbon stock in all types of woody vegetation - whether natural or plantations.\n\nAnnual stocks are expressed in units of tons of biomass. Annual changes in stocks are expressed in units of CO2 equivalent, i.e., the amount of CO2 released from or taken up by terrestrial ecosystems for that specific pixel.\n\nThe spatial data sets are available on [Microsoft\u2019s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass) under a Creative Common license of the type Attribution-Non Commercial-Share Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\n\n[Chloris Geospatial](https://chloris.earth/) is a mission-driven technology company that develops software and data products on the state of natural capital for use by business, governments, and the social sector.\n", "instrument": null, "keywords": "biomass,carbon,chloris,chloris-biomass,modis", "license": "CC-BY-NC-SA-4.0", "missionStartDate": "2003-07-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chloris Biomass"}, "cil-gdpcir-cc-by": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n| Modeling institution | Source model | Available experiments | License collection |\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\n| CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM | INM-CM4-8 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| BCC | BCC-CSM2-MR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\n| CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\n| CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40 |\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40 |\n| MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 |\n| MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40 |\n| NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| NOAA-GFDL | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40 |\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-AerChem | ssp370 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\n| CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3] |\n\n*Notes:*\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* **FGOALS-g3**\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* **INM-CM4-8**\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* **INM-CM5-0**\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* **ACCESS-CM2**\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* **ACCESS-ESM1-5**\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* **BCC-CSM2-MR**\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* **CMCC-CM2-SR5**\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* **CMCC-ESM2**\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* **EC-Earth3-AerChem**\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* **EC-Earth3-CC**\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* **EC-Earth3-Veg-LR**\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* **EC-Earth3-Veg**\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* **EC-Earth3**\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* **GFDL-CM4**\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* **GFDL-ESM4**\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* **HadGEM3-GC31-LL**\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* **MIROC-ES2L**\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* **MIROC6**\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* **MPI-ESM1-2-HR**\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* **MPI-ESM1-2-LR**\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* **NESM3**\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* **NorESM2-LM**\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* **NorESM2-MM**\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* **UKESM1-0-LL**\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n* **CanESM5**\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "keywords": "cil-gdpcir-cc-by,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-4.0", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)"}, "cil-gdpcir-cc-by-sa": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n* [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 179MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n| Modeling institution | Source model | Available experiments | License collection |\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\n| CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM | INM-CM4-8 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| BCC | BCC-CSM2-MR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40] |\n| CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40] |\n| CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40] |\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40] |\n| MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40] |\n| MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40] |\n| NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| NOAA-GFDL | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40] |\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40] |\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40] |\n| EC-Earth-Consortium | EC-Earth3-AerChem | ssp370 | CC-BY-40] |\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40] |\n| EC-Earth-Consortium | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40] |\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40] |\n| CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-SA-40] |\n\n*Notes:*\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* **FGOALS-g3**\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* **INM-CM4-8**\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* **INM-CM5-0**\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* **ACCESS-CM2**\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* **ACCESS-ESM1-5**\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* **BCC-CSM2-MR**\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* **CMCC-CM2-SR5**\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* **CMCC-ESM2**\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* **EC-Earth3-AerChem**\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* **EC-Earth3-CC**\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* **EC-Earth3-Veg-LR**\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* **EC-Earth3-Veg**\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* **EC-Earth3**\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* **GFDL-CM4**\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* **GFDL-ESM4**\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* **HadGEM3-GC31-LL**\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* **MIROC-ES2L**\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* **MIROC6**\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* **MPI-ESM1-2-HR**\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* **MPI-ESM1-2-LR**\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* **NESM3**\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* **NorESM2-LM**\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* **NorESM2-MM**\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* **UKESM1-0-LL**\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n#### CC-BY-SA-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this license requires citation of the source model output (included here) and requires that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\n\n* **CanESM5**\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "keywords": "cil-gdpcir-cc-by-sa,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-SA-4.0", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)"}, "cil-gdpcir-cc0": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n| Modeling institution | Source model | Available experiments | License collection |\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\n| CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM | INM-CM4-8 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| BCC | BCC-CSM2-MR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\n| CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\n| CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40 |\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40 |\n| MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 |\n| MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40 |\n| NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| NOAA-GFDL | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40 |\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-AerChem | ssp370 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\n| CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3] |\n\n*Notes:*\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* **FGOALS-g3**\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* **INM-CM4-8**\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* **INM-CM5-0**\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* **ACCESS-CM2**\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* **ACCESS-ESM1-5**\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* **BCC-CSM2-MR**\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* **CMCC-CM2-SR5**\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* **CMCC-ESM2**\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* **EC-Earth3-AerChem**\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* **EC-Earth3-CC**\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* **EC-Earth3-Veg-LR**\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* **EC-Earth3-Veg**\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* **EC-Earth3**\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* **GFDL-CM4**\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* **GFDL-ESM4**\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* **HadGEM3-GC31-LL**\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* **MIROC-ES2L**\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* **MIROC6**\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* **MPI-ESM1-2-HR**\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* **MPI-ESM1-2-LR**\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* **NESM3**\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* **NorESM2-LM**\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* **NorESM2-MM**\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* **UKESM1-0-LL**\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n* **CanESM5**\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "keywords": "cil-gdpcir-cc0,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC0-1.0", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC0-1.0)"}, "conus404": {"abstract": "[CONUS404](https://www.usgs.gov/data/conus404-four-kilometer-long-term-regional-hydroclimate-reanalysis-over-conterminous-united) is a unique, high-resolution hydro-climate dataset appropriate for forcing hydrological models and conducting meteorological analysis over the conterminous United States. CONUS404, so named because it covers the CONterminous United States for over 40 years at 4 km resolution, was produced by the Weather Research and Forecasting (WRF) model simulations run by NCAR as part of a collaboration with the USGS Water Mission Area. The CONUS404 includes 42 years of data (water years 1980-2021) and the spatial domain extends beyond the CONUS into Canada and Mexico, thereby capturing transboundary river basins and covering all contributing areas for CONUS surface waters.\n\nThe CONUS404 dataset, produced using WRF version 3.9.1.1, is the successor to the CONUS1 dataset in [ds612.0](https://rda.ucar.edu/datasets/ds612.0/) (Liu, et al., 2017) with improved representation of weather and climate conditions in the central United States due to the addition of a shallow groundwater module and several other improvements in the NOAH-Multiparameterization land surface model. It also uses a more up-to-date and higher-resolution reanalysis dataset (ERA5) as input and covers a longer period than CONUS1.", "instrument": null, "keywords": "climate,conus404,hydroclimate,hydrology,inland-waters,precipitation,weather", "license": "CC-BY-4.0", "missionStartDate": "1979-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CONUS404"}, "cop-dem-glo-30": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 30 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: \n\n", "instrument": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-30"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 90 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: \n\n", "instrument": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-90"}, "daymet-annual-hi": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "keywords": "climate,daymet,daymet-annual-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-07-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Annual Hawaii"}, "daymet-annual-na": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "keywords": "climate,daymet,daymet-annual-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-07-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Annual North America"}, "daymet-annual-pr": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "keywords": "climate,daymet,daymet-annual-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-07-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Annual Puerto Rico"}, "daymet-daily-hi": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "keywords": "daymet,daymet-daily-hi,hawaii,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "missionStartDate": "1980-01-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Daily Hawaii"}, "daymet-daily-na": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "keywords": "daymet,daymet-daily-na,north-america,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "missionStartDate": "1980-01-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Daily North America"}, "daymet-daily-pr": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "keywords": "daymet,daymet-daily-pr,precipitation,puerto-rico,temperature,vapor-pressure,weather", "license": "proprietary", "missionStartDate": "1980-01-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Daily Puerto Rico"}, "daymet-monthly-hi": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "keywords": "climate,daymet,daymet-monthly-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-01-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Monthly Hawaii"}, "daymet-monthly-na": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "keywords": "climate,daymet,daymet-monthly-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-01-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Monthly North America"}, "daymet-monthly-pr": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "keywords": "climate,daymet,daymet-monthly-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-01-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Monthly Puerto Rico"}, "deltares-floods": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level. At the coastline, the model is forced by extreme water levels containing surge and tide from GTSMip6. The water level at the coastline is extended landwards to all areas that are hydrodynamically connected to the coast following a \u2018bathtub\u2019 like approach and calculates the flood depth as the difference between the water level and the topography. Unlike a simple 'bathtub' model, this model attenuates the water level over land with a maximum attenuation factor of 0.5\u2009m\u2009km-1. The attenuation factor simulates the dampening of the flood levels due to the roughness over land.\n\nIn its current version, the model does not account for varying roughness over land and permanent water bodies such as rivers and lakes, and it does not account for the compound effects of waves, rainfall, and river discharge on coastal flooding. It also does not include the mitigating effect of coastal flood protection. Flood extents must thus be interpreted as the area that is potentially exposed to flooding without coastal protection.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf) for more information.\n\n## Digital elevation models (DEMs)\n\nThis documentation will refer to three DEMs:\n\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem) product.\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/), derived from SRTM and AW3D.\n* `LIDAR` is the [Global LiDAR Lowland DTM (GLL_DTM_v1)](https://data.mendeley.com/datasets/v5x4vpnzds/1).\n\n## Global datasets\n\nThis collection includes multiple global flood datasets derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different resolutions. Not all DEMs have all resolutions:\n\n* `NASADEM` and `MERITDEM` are available at `90m` and `1km` resolutions\n* `LIDAR` is available at `5km` resolution\n\n## Historic event datasets\n\nThis collection also includes historical storm event data files that follow similar DEM and resolution conventions. Not all storms events are available for each DEM and resolution combination, but generally follow the format of:\n\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\n\nFor example, a flood map for the MERITDEM-derived 90m flood data for the \"Omar\" storm in 2008 is available at:\n\n\n\n## Contact\n\nFor questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=deltares-floods%20question).", "instrument": null, "keywords": "deltares,deltares-floods,flood,global,sea-level-rise,water", "license": "CDLA-Permissive-1.0", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Deltares Global Flood Maps"}, "deltares-water-availability": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced a hydrological model approach to simulate historical daily reservoir variations for 3,236 locations across the globe for the period 1970-2020 using the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/) model. The model outputs long-term daily information on reservoir volume, inflow and outflow dynamics, as well as information on upstream hydrological forcing.\n\nThey hydrological model was forced with 5 different precipitation products. Two products (ERA5 and CHIRPS) are available at the global scale, while for Europe, USA and Australia a regional product was use (i.e. EOBS, NLDAS and BOM, respectively). Using these different precipitation products, it becomes possible to assess the impact of uncertainty in the model forcing. A different number of basins upstream of reservoirs are simulated, given the spatial coverage of each precipitation product.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf) for more information.\n\n## Dataset coverages\n\n| Name | Scale | Period | Number of basins |\n|--------|--------------------------|-----------|------------------|\n| ERA5 | Global | 1967-2020 | 3236 |\n| CHIRPS | Global (+/- 50 latitude) | 1981-2020 | 2951 |\n| EOBS | Europe/North Africa | 1979-2020 | 682 |\n| NLDAS | USA | 1979-2020 | 1090 |\n| BOM | Australia | 1979-2020 | 116 |\n\n## STAC Metadata\n\nThis STAC collection includes one STAC item per dataset. The item includes a `deltares:reservoir` property that can be used to query for the URL of a specific dataset.\n\n## Contact\n\nFor questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=deltares-floods%20question).", "instrument": null, "keywords": "deltares,deltares-water-availability,precipitation,reservoir,water,water-availability", "license": "CDLA-Permissive-1.0", "missionStartDate": "1970-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Deltares Global Water Availability"}, "drcog-lulc": {"abstract": "The [Denver Regional Council of Governments (DRCOG) Land Use/Land Cover (LULC)](https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project) datasets are developed in partnership with the [Babbit Center for Land and Water Policy](https://www.lincolninst.edu/our-work/babbitt-center-land-water-policy) and the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)'s Conservation Innovation Center (CIC). DRCOG LULC includes 2018 data at 3.28ft (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution covering 6,000 square miles of the Denver, Colorado region. The classification data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP) aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project) (6in resolution everywhere except the mountainous regions to the west, which are 1ft resolution).", "instrument": null, "keywords": "drcog-lulc,land-cover,land-use,naip,usda", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Denver Regional Council of Governments Land Use Land Cover"}, "eclipse": {"abstract": "The [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/) Network is a low-cost air quality sensing network for cities and a research project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/) at Microsoft Research.\n\nProject Eclipse currently includes over 100 locations in Chicago, Illinois, USA.\n\nThis network was deployed starting in July, 2021, through a collaboration with the City of Chicago, the Array of Things Project, JCDecaux Chicago, and the Environmental Law and Policy Center as well as local environmental justice organizations in the city. [This talk]( https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/) documents the network design and data calibration strategy.\n\n## Storage resources\n\nData are stored in [Parquet](https://parquet.apache.org/) files in Azure Blob Storage in the West Europe Azure region, in the following blob container:\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\n\nWithin that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`, where `YYYY-MM-DD` corresponds to the date of the snapshot.\nEach snapshot contains a sensor readings from the next 7-days in Parquet format starting with date on the folder name YYYY-MM-DD.\nTherefore, the data files for the first snapshot are at\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_*.parquet\n\nThe Parquet file schema is as described below. \n\n## Additional Documentation\n\nFor details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).\n\n## License and attribution\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022) [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)\n\n## Contact\n\nFor questions about this dataset, contact [`msrurbanops@microsoft.com`](mailto:msrurbanops@microsoft.com?subject=eclipse%20question) \n\n\n## Learn more\n\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/) contains an overview of the Project Eclipse at Microsoft Research.\n\n", "instrument": null, "keywords": "air-pollution,eclipse,pm25", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Urban Innovation Eclipse Sensor Data"}, "ecmwf-forecast": {"abstract": "The [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products) offers real-time meterological and oceanographic productions from the ECMWF forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction) for detailed information on each of the products.\n\n## Overview of products\n\nThe following diagram shows the publishing schedule of the various products.\n\n\n\nThe vertical axis shows the various products, defined below, which are grouped by combinations of `stream`, `forecast type`, and `reference time`. The horizontal axis shows *forecast times* in 3-hour intervals out from the reference time. A black square over a particular forecast time, or step, indicates that a forecast is made for that forecast time, for that particular `stream`, `forecast type`, `reference time` combination.\n\n* **stream** is the forecasting system that produced the data. The values are available in the `ecmwf:stream` summary of the STAC collection. They are:\n * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), atmospheric fields\n * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast) fields from the ECMWF model only.\n * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast), atmospheric fields \n * `scda`: short cut-off high-resolution forecast, atmospheric fields (also known as \"high-frequency products\")\n * `scwv`: short cut-off high-resolution forecast, ocean wave fields (also known as \"high-frequency products\") and\n * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), ocean wave fields,\n * `wave`: wave model\n* **type** is the forecast type. The values are available in the `ecmwf:type` summary of the STAC collection. They are:\n * `fc`: forecast\n * `ef`: ensemble forecast\n * `pf`: ensemble probabilities\n * `tf`: trajectory forecast for tropical cyclone tracks\n* **reference time** is the hours after midnight when the model was run. Each stream / type will produce assets for different forecast times (steps from the reference datetime) depending on the reference time.\n\nVisit the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) for more details on each of the various products.\n\nAssets are available for the previous 30 days.\n\n## Asset overview\n\nThe data are provided as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\nAdditionally, [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles) are provided, which can be used to read subsets of the data from Azure Blob Storage.\n\nWithin each `stream`, `forecast type`, `reference time`, the structure of the data are mostly consistent. Each GRIB2 file will have the\nsame data variables, coordinates (aside from `time` as the *reference time* changes and `step` as the *forecast time* changes). The exception\nis the `enfo-ep` and `waef-ep` products, which have more `step`s in the 240-hour forecast than in the 360-hour forecast. \n\nSee the example notebook for more on how to access the data.\n\n## STAC metadata\n\nThe Planetary Computer provides a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so every item has the same\n`bbox` and `geometry`.\n\nA few custom properties are available on each STAC item, which can be used in searches to narrow down the data to items of interest:\n\n* `ecmwf:stream`: The forecasting system (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:type`: The forecast type (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:step`: The offset from the reference datetime, expressed as ``, for example `\"3h\"` means \"3 hours from the reference datetime\". \n* `ecmwf:reference_datetime`: The datetime when the model was run. This indicates when the forecast *was made*, rather than when it's valid for.\n* `ecmwf:forecast_datetime`: The datetime for which the forecast is valid. This is also set as the item's `datetime`.\n\nSee the example notebook for more on how to use the STAC metadata to query for particular data.\n\n## Attribution\n\nThe products listed and described on this page are available to the public and their use is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that the data may be redistributed and used commercially, subject to appropriate attribution.\n\nThe following wording should be attached to the use of this ECMWF dataset: \n\n1. Copyright statement: Copyright \"\u00a9 [year] European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\n3. License Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications.\n\nThe following wording shall be attached to services created with this ECMWF dataset:\n\n1. Copyright statement: Copyright \"This service is based on data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source www.ecmwf.int\n3. License Statement: This ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications\n\n## More information\n\nFor more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).", "instrument": null, "keywords": "ecmwf,ecmwf-forecast,forecast,weather", "license": "CC-BY-4.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ECMWF Open Data (real-time)"}, "era5-pds": {"abstract": "ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate\ncovering the period from January 1950 to present. ERA5 is produced by the\nCopernicus Climate Change Service (C3S) at ECMWF.\n\nReanalysis combines model data with observations from across the world into a\nglobally complete and consistent dataset using the laws of physics. This\nprinciple, called data assimilation, is based on the method used by numerical\nweather prediction centres, where every so many hours (12 hours at ECMWF) a\nprevious forecast is combined with newly available observations in an optimal\nway to produce a new best estimate of the state of the atmosphere, called\nanalysis, from which an updated, improved forecast is issued. Reanalysis works\nin the same way, but at reduced resolution to allow for the provision of a\ndataset spanning back several decades. Reanalysis does not have the constraint\nof issuing timely forecasts, so there is more time to collect observations, and\nwhen going further back in time, to allow for the ingestion of improved versions\nof the original observations, which all benefit the quality of the reanalysis\nproduct.\n\nThis dataset was converted to Zarr by [Planet OS](https://planetos.com/).\nSee [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\nfor more.\n\n## STAC Metadata\n\nTwo types of data variables are provided: \"forecast\" (`fc`) and \"analysis\" (`an`).\n\n* An **analysis**, of the atmospheric conditions, is a blend of observations\n with a previous forecast. An analysis can only provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters (parameters valid at a specific time, e.g temperature at 12:00),\n but not accumulated parameters, mean rates or min/max parameters.\n* A **forecast** starts with an analysis at a specific time (the 'initialization\n time'), and a model computes the atmospheric conditions for a number of\n 'forecast steps', at increasing 'validity times', into the future. A forecast\n can provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters, accumulated parameters, mean rates, and min/max parameters.\n\nEach [STAC](https://stacspec.org/) item in this collection covers a single month\nand the entire globe. There are two STAC items per month, one for each type of data\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties to\nindicate which kind of variables that STAC item catalogs.\n\n## How to acknowledge, cite and refer to ERA5\n\nAll users of data on the Climate Data Store (CDS) disks (using either the web interface or the CDS API) must provide clear and visible attribution to the Copernicus programme and are asked to cite and reference the dataset provider:\n\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nCite each dataset used as indicated on the relevant CDS entries (see link to \"Citation\" under References on the Overview page of the dataset entry).\n\nThroughout the content of your publication, the dataset used is referred to as Author (YYYY).\n\nThe 3-steps procedure above is illustrated with this example: [Use Case 2: ERA5 hourly data on single levels from 1979 to present](https://confluence.ecmwf.int/display/CKB/Use+Case+2%3A+ERA5+hourly+data+on+single+levels+from+1979+to+present).\n\nFor complete details, please refer to [How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it).", "instrument": null, "keywords": "ecmwf,era5,era5-pds,precipitation,reanalysis,temperature,weather", "license": "proprietary", "missionStartDate": "1979-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ERA5 - PDS"}, "esa-cci-lc": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf) to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).\n", "instrument": null, "keywords": "cci,esa,esa-cci-lc,global,land-cover", "license": "proprietary", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA Climate Change Initiative Land Cover Maps (Cloud Optimized GeoTIFF)"}, "esa-cci-lc-netcdf": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection are the original NetCDF files accessed from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc), which provides the data as Cloud Optimized GeoTIFFs.", "instrument": null, "keywords": "cci,esa,esa-cci-lc-netcdf,global,land-cover", "license": "proprietary", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA Climate Change Initiative Land Cover Maps (NetCDF)"}, "esa-worldcover": {"abstract": "The European Space Agency (ESA) [WorldCover](https://esa-worldcover.org/en) product provides global land cover maps for the years 2020 and 2021 at 10 meter resolution based on the combination of [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) radar data and [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) imagery. The discrete classification maps provide 11 classes defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The map images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n\nThe WorldCover product is developed by a consortium of European service providers and research organizations. [VITO](https://remotesensing.vito.be/) (Belgium) is the prime contractor of the WorldCover consortium together with [Brockmann Consult](https://www.brockmann-consult.de/) (Germany), [CS SI](https://www.c-s.fr/) (France), [Gamma Remote Sensing AG](https://www.gamma-rs.ch/) (Switzerland), [International Institute for Applied Systems Analysis](https://www.iiasa.ac.at/) (Austria), and [Wageningen University](https://www.wur.nl/nl/Wageningen-University.htm) (The Netherlands).\n\nTwo versions of the WorldCover product are available:\n\n- WorldCover 2020 produced using v100 of the algorithm\n - [WorldCover 2020 v100 User Manual](https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf)\n - [WorldCover 2020 v100 Validation Report]()\n\n- WorldCover 2021 produced using v200 of the algorithm\n - [WorldCover 2021 v200 User Manual]()\n - [WorldCover 2021 v200 Validaton Report]()\n\nSince the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps include both changes in real land cover and changes due to the used algorithms.\n", "instrument": "c-sar,msi", "keywords": "c-sar,esa,esa-worldcover,global,land-cover,msi,sentinel,sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "license": "CC-BY-4.0", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "processingLevel": null, "title": "ESA WorldCover"}, "fia": {"abstract": "Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service's [Forest Inventory and Analysis](https://www.fia.fs.fed.us/) (FIA) program.\n\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation's forest land.\n\nDomain: continental U.S., 1928-2018\n\nResolution: plot-level (irregular polygon)\n\nThis dataset was curated and brought to Azure by [CarbonPlan](https://carbonplan.org/).\n", "instrument": null, "keywords": "biomass,carbon,fia,forest,forest-service,species,usda", "license": "CC0-1.0", "missionStartDate": "2020-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Forest Inventory and Analysis"}, "fws-nwi": {"abstract": "The Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated.\n\n**NOTE:** Due to the variation in use and analysis of this data by the end user, each state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.\n\nFor more information, visit the National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\n\n## STAC Metadata\n\nIn addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet) asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles) to select just the geoparquet assets. See the Example Notebook for more.", "instrument": null, "keywords": "fws-nwi,united-states,usfws,wetlands", "license": "proprietary", "missionStartDate": "2022-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FWS National Wetlands Inventory"}, "gap": {"abstract": "The [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5), based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states), are the foundation of the most detailed, consistent map of vegetation available for the United States. These data facilitate planning and management for biological diversity on a regional and national scale.\n\nThis dataset includes the [land cover](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover) component of the GAP/LANDFIRE project.\n\n", "instrument": null, "keywords": "gap,land-cover,landfire,united-states,usgs", "license": "proprietary", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS Gap Land Cover"}, "gbif": {"abstract": "The [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is an international network and data infrastructure funded by the world's governments, providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences.\n\nThe GBIF occurrence dataset combines data from a wide array of sources, including specimen-related data from natural history museums, observations from citizen science networks, and automated environmental surveys. While these data are constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots are taken and made available here. \n\nData are stored in [Parquet](https://parquet.apache.org/) format; the Parquet file schema is described below. Most field names correspond to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and have been interpreted by GBIF's systems to align taxonomy, location, dates, etc. Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\n\nPlease refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines) for information about how to cite GBIF data in publications.. For analyses using the whole dataset, please use the following citation:\n\n> GBIF.org ([Date]) GBIF Occurrence Data [DOI of dataset]\n\nFor analyses where data are significantly filtered, please track the datasetKeys used and use a \"[derived dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\" record for citing the data.\n\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/) contains a number of articles that can help you analyze GBIF data.\n", "instrument": null, "keywords": "biodiversity,gbif,species", "license": "proprietary", "missionStartDate": "2021-04-13T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Biodiversity Information Facility (GBIF)"}, "gnatsgo-rasters": {"abstract": "This collection contains the raster data for gNATSGO. In order to use the map unit values contained in the `mukey` raster asset, you'll need to join to tables represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables) Collection. Many items have commonly used values encoded in additional raster assets.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "keywords": "gnatsgo-rasters,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "missionStartDate": "2020-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "gNATSGO Soil Database - Rasters"}, "gnatsgo-tables": {"abstract": "This collection contains the table data for gNATSGO. This table data can be used to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters) Collection.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "keywords": "gnatsgo-tables,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "missionStartDate": "2020-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "gNATSGO Soil Database - Tables"}, "goes-cmi": {"abstract": "The GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product provides 16 reflective and emissive bands at high temporal cadence over the Western Hemisphere.\n\nThe GOES-R series is the latest in the Geostationary Operational Environmental Satellites (GOES) program, which has been operated in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer. The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions between 0.5km and 2km.\n\n### Geographic coverage\n\nThe ABI captures three levels of coverage, each at a different temporal cadence depending on the modes described below. The geographic coverage for each image is described by the `goes:image-type` STAC Item property.\n\n- _FULL DISK_: a circular image depicting nearly full coverage of the Western Hemisphere.\n- _CONUS_: a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\n- _MESOSCALE_: a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between two different mesoscale geographic regions.\n\n### Modes\n\nThere are three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and Mode 6.\n\n- Mode _3_ consists of one observation of the full disk scene of the Earth, three observations of the continental United States (CONUS), and thirty observations for each of two distinct mesoscale views every fifteen minutes.\n- Mode _4_ consists of the observation of the full disk scene every five minutes.\n- Mode _6_ consists of one observation of the full disk scene of the Earth, two observations of the continental United States (CONUS), and twenty observations for each of two distinct mesoscale views every ten minutes.\n\nThe mode that each image was captured with is described by the `goes:mode` STAC Item property.\n\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8) video for an idea of how the ABI scans multiple geographic regions over time.\n\n### Cloud and Moisture Imagery\n\nThe Cloud and Moisture Imagery product contains one or more images with pixel values identifying \"brightness values\" that are scaled to support visual analysis. Cloud and Moisture Imagery product (CMIP) files are generated for each of the sixteen ABI reflective and emissive bands. In addition, there is a multi-band product file that includes the imagery at all bands (MCMIP).\n\nThe Planetary Computer STAC Collection `goes-cmi` captures both the CMIP and MCMIP product files into individual STAC Items for each observation from a GOES-R satellite. It contains the original CMIP and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of the data from each MCMIP band (2km); the full-resolution CMIP band for bands 1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful for rendering.\n\nThis product is not in a standard coordinate reference system (CRS), which can cause issues with some tooling that does not handle non-standard large geographic regions.\n\n### For more information\n- [Beginner\u2019s Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\n- [GOES-R Series Product Definition and Users\u2019 Guide: Volume 5 (Level 2A+ Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish verison](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/raw/main/GOES/QuickGuides/Spanish/Guia%20introductoria%20para%20datos%20de%20la%20serie%20GOES-R%20V1.1%20FINAL2%20-%20Copy.pdf))\n\n", "instrument": "ABI", "keywords": "abi,cloud,goes,goes-16,goes-17,goes-18,goes-cmi,moisture,nasa,noaa,satellite", "license": "proprietary", "missionStartDate": "2017-02-28T00:16:52Z", "platform": null, "platformSerialIdentifier": "GOES-16,GOES-17,GOES-18", "processingLevel": null, "title": "GOES-R Cloud & Moisture Imagery"}, "goes-glm": {"abstract": "The [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html) is a single-channel, near-infrared optical transient detector that can detect the momentary changes in an optical scene, indicating the presence of lightning. GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground) activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. GLM collects information such as the frequency, location and extent of lightning discharges to identify intensifying thunderstorms and tropical cyclones. Trends in total lightning available from the GLM provide critical information to forecasters, allowing them to focus on developing severe storms much earlier and before these storms produce damaging winds, hail or even tornadoes.\n\nThe GLM data product consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, and flashes:\n\n- Lightning events are detected by the instrument.\n- Lightning groups are a collection of one or more lightning events that satisfy temporal and spatial coincidence thresholds.\n- Similarly, lightning flashes are a collection of one or more lightning groups that satisfy temporal and spatial coincidence thresholds.\n\nThe product includes the relationship among lightning events, groups, and flashes, and the area coverage of lightning groups and flashes. The product also includes processing and data quality metadata, and satellite state and location information. \n\nThis Collection contains GLM L2 data in tabular ([GeoParquet](https://github.com/opengeospatial/geoparquet)) format and the original source NetCDF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": "FM1,FM2", "keywords": "fm1,fm2,goes,goes-16,goes-17,goes-glm,l2,lightning,nasa,noaa,satellite,weather", "license": "proprietary", "missionStartDate": "2018-02-13T16:10:00Z", "platform": "GOES", "platformSerialIdentifier": "GOES-16,GOES-17", "processingLevel": ["L2"], "title": "GOES-R Lightning Detection"}, "gpm-imerg-hhr": {"abstract": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the [GPM satellite constellation](https://gpm.nasa.gov/missions/gpm/constellation) to estimate precipitation over the majority of the Earth's surface. This algorithm is particularly valuable over the majority of the Earth's surface that lacks precipitation-measuring instruments on the ground. Now in the latest Version 06 release of IMERG the algorithm fuses the early precipitation estimates collected during the operation of the TRMM satellite (2000 - 2015) with more recent precipitation estimates collected during operation of the GPM satellite (2014 - present). The longer the record, the more valuable it is, as researchers and application developers will attest. By being able to compare and contrast past and present data, researchers are better informed to make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security.\n\nFor more, see the [IMERG homepage](https://gpm.nasa.gov/data/imerg) The [IMERG Technical documentation](https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf) provides more information on the algorithm, input datasets, and output products.", "instrument": null, "keywords": "gpm,gpm-imerg-hhr,imerg,precipitation", "license": "proprietary", "missionStartDate": "2000-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GPM IMERG"}, "gridmet": {"abstract": "gridMET is a dataset of daily surface meteorological data at approximately four-kilometer resolution, covering the contiguous U.S. from 1979 to the present. These data can provide important inputs for ecological, agricultural, and hydrological models.\n", "instrument": null, "keywords": "climate,gridmet,precipitation,temperature,vapor-pressure,water", "license": "CC0-1.0", "missionStartDate": "1979-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "gridMET"}, "hgb": {"abstract": "This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution. The aboveground biomass map integrates land-cover-specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover-specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree/land cover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.\n", "instrument": null, "keywords": "biomass,carbon,hgb,ornl", "license": "proprietary", "missionStartDate": "2010-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "HGB: Harmonized Global Biomass for 2010"}, "hrea": {"abstract": "The [HREA](http://www-personal.umich.edu/~brianmin/HREA/index.html) project aims to provide open access to new indicators of electricity access and reliability across the world. Leveraging satellite imagery with computational methods, these high-resolution data provide new tools to track progress toward reliable and sustainable energy access across the world.\n\nThis dataset includes settlement-level measures of electricity access, reliability, and usage for 89 nations, derived from nightly VIIRS satellite imagery. Specifically, this dataset provides the following annual values at country-level granularity:\n\n1. **Access**: Predicted likelihood that a settlement is electrified, based on night-by-night comparisons of each settlement against matched uninhabited areas over a calendar year.\n\n2. **Reliability**: Proportion of nights a settlement is statistically brighter than matched uninhabited areas. Areas with more frequent power outages or service interruptions have lower rates.\n\n3. **Usage**: Higher levels of brightness indicate more robust usage of outdoor lighting, which is highly correlated with overall energy consumption.\n\n4. **Nighttime Lights**: Annual composites of VIIRS nighttime light output.\n\nFor more information and methodology, please visit the [HREA website](http://www-personal.umich.edu/~brianmin/HREA/index.html).\n", "instrument": null, "keywords": "electricity,hrea,viirs", "license": "CC-BY-4.0", "missionStartDate": "2012-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "HREA: High Resolution Electricity Access"}, "io-biodiversity": {"abstract": "Generated by [Impact Observatory](https://www.impactobservatory.com/), in collaboration with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\n\nMaps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579) of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future.\n\nBiodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf) entitled \u201cGlobal 100m Projections of Biodiversity Intactness for the years 2017-2020.\u201d\n\nAll years are available under a Creative Commons BY-4.0 license.\n", "instrument": null, "keywords": "biodiversity,global,io-biodiversity", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Biodiversity Intactness"}, "io-lulc": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2020 data available in the newer [9-class dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)._\n\nGlobal estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution. This dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the relevant yearly Sentinel-2 scenes on the Planetary Computer.\n\nThis dataset is also available on the [ArcGIS Living Atlas of the World](https://livingatlas.arcgis.com/landcover/).\n", "instrument": null, "keywords": "global,io-lulc,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Esri 10-Meter Land Cover (10-class)"}, "io-lulc-9-class": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2023 data available in the newer [9-class v2 dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02)._\n\nTime series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "keywords": "global,io-lulc-9-class,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "10m Annual Land Use Land Cover (9-class) V1"}, "io-lulc-annual-v02": {"abstract": "Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset, produced by [Impact Observatory](http://impactobservatory.com/), Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThese maps have been improved from Impact Observatory\u2019s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class) and provide a relative reduction in the amount of anomalous change between classes, particularly between \u201cBare\u201d and any of the vegetative classes \u201cTrees,\u201d \u201cCrops,\u201d \u201cFlooded Vegetation,\u201d and \u201cRangeland\u201d. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "keywords": "global,io-lulc-annual-v02,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "10m Annual Land Use Land Cover (9-class) V2"}, "jrc-gsw": {"abstract": "Global surface water products from the European Commission Joint Research Centre, based on Landsat 5, 7, and 8 imagery. Layers in this collection describe the occurrence, change, and seasonality of surface water from 1984-2020. Complete documentation for each layer is available in the [Data Users Guide](https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf).\n", "instrument": null, "keywords": "global,jrc-gsw,landsat,water", "license": "proprietary", "missionStartDate": "1984-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "JRC Global Surface Water"}, "kaza-hydroforecast": {"abstract": "This dataset is a daily updated set of HydroForecast seasonal river flow forecasts at six locations in the Kwando and Upper Zambezi river basins. More details about the locations, project context, and to interactively view current and previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\n\n## Flow forecast dataset and model description\n\n[HydroForecast](https://www.upstream.tech/hydroforecast) is a theory-guided machine learning hydrologic model that predicts streamflow in basins across the world. For the Kwando and Upper Zambezi, HydroForecast makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview). The model's output is probabilistic and the mean, median and a range of quantiles are available at each forecast step.\n\nThe underlying model has the following attributes: \n\n* Timestep: 10 days\n* Horizon: 10 to 180 days \n* Update frequency: daily\n* Units: cubic meters per second (m\u00b3/s)\n \n## Site details\n\nThe model produces output for six locations in the Kwando and Upper Zambezi river basins.\n\n* Upper Zambezi sites\n * Zambezi at Chavuma\n * Luanginga at Kalabo\n* Kwando basin sites\n * Kwando at Kongola -- total basin flows\n * Kwando Sub-basin 1\n * Kwando Sub-basin 2 \n * Kwando Sub-basin 3\n * Kwando Sub-basin 4\n * Kwando Kongola Sub-basin\n\n## STAC metadata\n\nThere is one STAC item per location. Each STAC item has a single asset linking to a Parquet file in Azure Blob Storage.", "instrument": null, "keywords": "hydroforecast,hydrology,kaza-hydroforecast,streamflow,upstream-tech,water", "license": "CDLA-Sharing-1.0", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "HydroForecast - Kwando & Upper Zambezi Rivers"}, "landsat-c2-l1": {"abstract": "Landsat Collection 2 Level-1 data, consisting of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data. These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.\n\nThis dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "mss", "keywords": "global,imagery,landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-c2-l1,mss,nasa,satellite,usgs", "license": "proprietary", "missionStartDate": "1972-07-25T00:00:00Z", "platform": null, "platformSerialIdentifier": "landsat-1,landsat-2,landsat-3,landsat-4,landsat-5", "processingLevel": null, "title": "Landsat Collection 2 Level-1"}, "landsat-c2-l2": {"abstract": "Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\n\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "tm,etm+,oli,tirs", "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "missionStartDate": "1982-08-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "title": "Landsat Collection 2 Level-2"}, "mobi": {"abstract": "The [Map of Biodiversity Importance](https://www.natureserve.org/conservation-tools/projects/map-biodiversity-importance) (MoBI) consists of raster maps that combine habitat information for 2,216 imperiled species occurring in the conterminous United States, using weightings based on range size and degree of protection to identify areas of high importance for biodiversity conservation. Species included in the project are those which, as of September 2018, had a global conservation status of G1 (critical imperiled) or G2 (imperiled) or which are listed as threatened or endangered at the full species level under the United States Endangered Species Act. Taxonomic groups included in the project are vertebrates (birds, mammals, amphibians, reptiles, turtles, crocodilians, and freshwater and anadromous fishes), vascular plants, selected aquatic invertebrates (freshwater mussels and crayfish) and selected pollinators (bumblebees, butterflies, and skippers).\n\nThere are three types of spatial data provided, described in more detail below: species richness, range-size rarity, and protection-weighted range-size rarity. For each type, this data set includes five different layers – one for all species combined, and four additional layers that break the data down by taxonomic group (vertebrates, plants, freshwater invertebrates, and pollinators) – for a total of fifteen layers.\n\nThese data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\n", "instrument": null, "keywords": "biodiversity,mobi,natureserve,united-states", "license": "proprietary", "missionStartDate": "2020-04-14T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MoBI: Map of Biodiversity Importance"}, "modis-09A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) 09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "keywords": "aqua,global,imagery,mod09a1,modis,modis-09a1-061,myd09a1,nasa,reflectance,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Surface Reflectance 8-Day (500m)"}, "modis-09Q1-061": {"abstract": "The 09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "keywords": "aqua,global,imagery,mod09q1,modis,modis-09q1-061,myd09q1,nasa,reflectance,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Surface Reflectance 8-Day (250m)"}, "modis-10A1-061": {"abstract": "This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS Snow Cover 5-Min L2 Swath 500m' data set. Each data granule is a 10degx10deg tile projected to a 500 m sinusoidal grid.", "instrument": "modis", "keywords": "aqua,global,mod10a1,modis,modis-10a1-061,myd10a1,nasa,satellite,snow,terra", "license": "proprietary", "missionStartDate": "2000-02-24T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Snow Cover Daily"}, "modis-10A2-061": {"abstract": "This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS Snow Cover Daily L3 Global 500m Grid' data set. A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.", "instrument": "modis", "keywords": "aqua,global,mod10a2,modis,modis-10a2-061,myd10a2,nasa,satellite,snow,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Snow Cover 8-day"}, "modis-11A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types", "instrument": "modis", "keywords": "aqua,global,mod11a1,modis,modis-11a1-061,myd11a1,nasa,satellite,temperature,terra", "license": "proprietary", "missionStartDate": "2000-02-24T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Land Surface Temperature/Emissivity Daily"}, "modis-11A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.", "instrument": "modis", "keywords": "aqua,global,mod11a2,modis,modis-11a2-061,myd11a2,nasa,satellite,temperature,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Land Surface Temperature/Emissivity 8-Day"}, "modis-13A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "keywords": "aqua,global,mod13a1,modis,modis-13a1-061,myd13a1,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Vegetation Indices 16-Day (500m)"}, "modis-13Q1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "keywords": "aqua,global,mod13q1,modis,modis-13q1-061,myd13q1,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Vegetation Indices 16-Day (250m)"}, "modis-14A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition.", "instrument": "modis", "keywords": "aqua,fire,global,mod14a1,modis,modis-14a1-061,myd14a1,nasa,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Thermal Anomalies/Fire Daily"}, "modis-14A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators.", "instrument": "modis", "keywords": "aqua,fire,global,mod14a2,modis,modis-14a2-061,myd14a2,nasa,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Thermal Anomalies/Fire 8-Day"}, "modis-15A2H-061": {"abstract": "The Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "keywords": "aqua,global,mcd15a2h,mod15a2h,modis,modis-15a2h-061,myd15a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2002-07-04T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Leaf Area Index/FPAR 8-Day"}, "modis-15A3H-061": {"abstract": "The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "keywords": "aqua,global,mcd15a3h,modis,modis-15a3h-061,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2002-07-04T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Leaf Area Index/FPAR 4-Day"}, "modis-16A3GF-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The product will be generated at the end of each year when the entire yearly 8-day MOD15A2H/MYD15A2H is available. Hence, the gap-filled product is the improved 16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year. Provided in the product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.", "instrument": "modis", "keywords": "aqua,global,mod16a3gf,modis,modis-16a3gf-061,myd16a3gf,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2001-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Net Evapotranspiration Yearly Gap-Filled"}, "modis-17A2H-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.", "instrument": "modis", "keywords": "aqua,global,mod17a2h,modis,modis-17a2h-061,myd17a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Gross Primary Productivity 8-Day"}, "modis-17A2HGF-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. This product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled A2HGF is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "keywords": "aqua,global,mod17a2hgf,modis,modis-17a2hgf-061,myd17a2hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Gross Primary Productivity 8-Day Gap-Filled"}, "modis-17A3HGF-061": {"abstract": "The Version 6.1 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual Moderate Resolution Imaging Spectroradiometer (MODIS) NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled product is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "keywords": "aqua,global,mod17a3hgf,modis,modis-17a3hgf-061,myd17a3hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Net Primary Production Yearly Gap-Filled"}, "modis-21A2-061": {"abstract": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the MOD11 LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. This dataset is an 8-day composite LST product at 1,000 meter spatial resolution that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free 21A1D and 21A1N daily acquisitions from the 8-day period. Unlike the 21A1 data sets where the daytime and nighttime acquisitions are separate products, the 21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).", "instrument": "modis", "keywords": "aqua,global,mod21a2,modis,modis-21a2-061,myd21a2,nasa,satellite,temperature,terra", "license": "proprietary", "missionStartDate": "2000-02-16T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Land Surface Temperature/3-Band Emissivity 8-Day"}, "modis-43A4-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.", "instrument": "modis", "keywords": "aqua,global,imagery,mcd43a4,modis,modis-43a4-061,nasa,reflectance,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-16T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Nadir BRDF-Adjusted Reflectance (NBAR) Daily"}, "modis-64A1-061": {"abstract": "The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.", "instrument": "modis", "keywords": "aqua,fire,global,imagery,mcd64a1,modis,modis-64a1-061,nasa,satellite,terra", "license": "proprietary", "missionStartDate": "2000-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Burned Area Monthly"}, "ms-buildings": {"abstract": "Bing Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases.\n\nFor more information, see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/) repository on GitHub.\n\n## Building footprint creation\n\nThe building extraction is done in two stages:\n\n1. Semantic Segmentation \u2013 Recognizing building pixels on an aerial image using deep neural networks (DNNs)\n2. Polygonization \u2013 Converting building pixel detections into polygons\n\n**Stage 1: Semantic Segmentation**\n\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\n\n**Stage 2: Polygonization**\n\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\n\n## Data assets\n\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet) datasets in [Delta][delta] table format.\nThe data are partitioned by\n\n1. Region\n2. quadkey at [Bing Map Tiles][tiles] level 9\n\nEach `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area.\n\nNote that older items in this dataset are *not* spatially partitioned. We recommend using data with a processing date\nof 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata\nfor each item (see below).\n\n## Delta Format\n\nThe collection-level asset under the `delta` key gives you the fsspec-style URL\nto the Delta table. This can be used to efficiently query for matching partitions\nby `Region` and `quadkey`. See the notebook for an example using Python.\n\n## STAC metadata\n\nThis STAC collection has one STAC item per region. The `msbuildings:region`\nproperty can be used to filter items to a specific region, and the `msbuildings:quadkey`\nproperty can be used to filter items to a specific quadkey (though you can also search\nby the `geometry`).\n\nNote that older STAC items are not spatially partitioned. We recommend filtering on\nitems with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection\nsummary for `msbuildings:processing-date` for a list of valid values.\n\n[delta]: https://delta.io/\n[tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\n", "instrument": null, "keywords": "bing-maps,buildings,delta,footprint,geoparquet,microsoft,ms-buildings", "license": "ODbL-1.0", "missionStartDate": "2014-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Microsoft Building Footprints"}, "mtbs": {"abstract": "[Monitoring Trends in Burn Severity](https://www.mtbs.gov/) (MTBS) is an inter-agency program whose goal is to consistently map the burn severity and extent of large fires across the United States from 1984 to the present. This includes all fires 1000 acres or greater in the Western United States and 500 acres or greater in the Eastern United States. The burn severity mosaics in this dataset consist of thematic raster images of MTBS burn severity classes for all currently completed MTBS fires for the continental United States and Alaska.\n", "instrument": null, "keywords": "fire,forest,mtbs,usda,usfs,usgs", "license": "proprietary", "missionStartDate": "1984-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MTBS: Monitoring Trends in Burn Severity"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) \nprovides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). \nNAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) \nwithin the [US Department of Agriculture](https://www.usda.gov/) (USDA). \nData are captured at least once every three years for each state. \nThis dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\nYou can visualize the coverage of current and past collections [here](https://naip-usdaonline.hub.arcgis.com/). \n", "instrument": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "missionStartDate": "2010-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NAIP: National Agriculture Imagery Program"}, "nasa-nex-gddp-cmip6": {"abstract": "The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four \u201cTier 1\u201d greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.\n\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/) maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6) where you can find more information about these datasets. Users are encouraged to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf), provided alongside the data set, where more detailed information, references and acknowledgements can be found.\n\nThis collection contains many NetCDF files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\n\n- **model** is the name of a modeling group (e.g. \"ACCESS-CM-2\"). See the `cmip6:model` summary in the STAC collection for a full list of models.\n- **scenario** is one of \"historical\", \"ssp245\" or \"ssp585\".\n- **variable** is one of \"hurs\", \"huss\", \"pr\", \"rlds\", \"rsds\", \"sfcWind\", \"tas\", \"tasmax\", \"tasmin\".\n- **year** depends on the value of *scenario*. For \"historical\", the values range from 1950 to 2014 (inclusive). For \"ssp245\" and \"ssp585\", the years range from 2015 to 2100 (inclusive).\n\nIn addition to the NetCDF files, we provide some *experimental* **reference files** as collection-level dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\nThese files include the positions of data variables within the binary NetCDF files, which can speed up reading the metadata. See the example notebook for more.", "instrument": null, "keywords": "climate,cmip6,humidity,nasa,nasa-nex-gddp-cmip6,precipitation,temperature", "license": "proprietary", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)"}, "nasadem": {"abstract": "[NASADEM](https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem) provides global topographic data at 1 arc-second (~30m) horizontal resolution, derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/) (SRTM).\n\n", "instrument": null, "keywords": "dem,elevation,jpl,nasa,nasadem,nga,srtm,usgs", "license": "proprietary", "missionStartDate": "2000-02-20T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NASADEM HGT v001"}, "noaa-c-cap": {"abstract": "Nationally standardized, raster-based inventories of land cover for the coastal areas of the U.S. Data are derived, through the Coastal Change Analysis Program, from the analysis of multiple dates of remotely sensed imagery. Two file types are available: individual dates that supply a wall-to-wall map, and change files that compare one date to another. The use of standardized data and procedures assures consistency through time and across geographies. C-CAP data forms the coastal expression of the National Land Cover Database (NLCD) and the A-16 land cover theme of the National Spatial Data Infrastructure. The data are updated every 5 years.", "instrument": null, "keywords": "coastal,land-cover,land-use,noaa,noaa-c-cap", "license": "proprietary", "missionStartDate": "1975-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "C-CAP Regional Land Cover and Change"}, "noaa-cdr-ocean-heat-content": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content,ocean,temperature", "license": "proprietary", "missionStartDate": "1972-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Heat Content CDR"}, "noaa-cdr-ocean-heat-content-netcdf": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-ocean-heat-content`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content-netcdf,ocean,temperature", "license": "proprietary", "missionStartDate": "1972-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Heat Content CDR NetCDFs"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"abstract": "The NOAA 1/4\u00b0 daily Optimum Interpolation Sea Surface Temperature (or daily OISST) Climate Data Record (CDR) provides complete ocean temperature fields constructed by combining bias-adjusted observations from different platforms (satellites, ships, buoys) on a regular global grid, with gaps filled in by interpolation. The main input source is satellite data from the Advanced Very High Resolution Radiometer (AVHRR), which provides high temporal-spatial coverage from late 1981-present. This input must be adjusted to the buoys due to erroneous cold SST data following the Mt Pinatubo and El Chichon eruptions. Applications include climate modeling, resource management, ecological studies on annual to daily scales.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-optimum-interpolation-netcdf`.\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-optimum-interpolation,ocean,temperature", "license": "proprietary", "missionStartDate": "1981-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Surface Temperature - Optimum Interpolation CDR"}, "noaa-cdr-sea-surface-temperature-whoi": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi,ocean,temperature", "license": "proprietary", "missionStartDate": "1988-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Surface Temperature - WHOI CDR"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-sea-surface-temperature-whoi`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi-netcdf,ocean,temperature", "license": "proprietary", "missionStartDate": "1988-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Surface Temperature - WHOI CDR NetCDFs"}, "noaa-climate-normals-gridded": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nThe data in this Collection have been converted from the original NetCDF format to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n## STAC Metadata\n\nThe STAC items in this collection contain several custom fields that can be used to further filter the data.\n\n* `noaa_climate_normals:period`: Climate normal time period. This can be \"1901-2000\", \"1991-2020\", or \"2006-2020\".\n* `noaa_climate_normals:frequency`: Climate normal temporal interval (frequency). This can be \"daily\", \"monthly\", \"seasonal\" , or \"annual\"\n* `noaa_climate_normals:time_index`: Time step index, e.g., month of year (1-12).\n\nThe `description` field of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions are\n\n* annual: \"Annual precipitation normals from monthly precipitation normal values\"\n* seasonal: \"Seasonal precipitation normals (WSSF) from monthly normals\"\n* monthly: \"Monthly precipitation normals from monthly precipitation values\"\n* daily: \"Precipitation normals from daily averages\"\n\nCheck the assets on individual items for the appropriate description.\n\nThe STAC keys for most assets consist of two abbreviations. A \"variable\":\n\n\n| Abbreviation | Description |\n| ------------ | ---------------------------------------- |\n| prcp | Precipitation over the time period |\n| tavg | Mean temperature over the time period |\n| tmax | Maximum temperature over the time period |\n| tmin | Minimum temperature over the time period |\n\nAnd an \"aggregation\":\n\n| Abbreviation | Description |\n| ------------ | ------------------------------------------------------------------------------ |\n| max | Maximum of the variable over the time period |\n| min | Minimum of the variable over the time period |\n| std | Standard deviation of the value over the time period |\n| flag | An count of the number of inputs (months, years, etc.) to calculate the normal |\n| norm | The normal for the variable over the time period |\n\nSo, for example, `prcp_max` for monthly data is the \"Maximum values of all input monthly precipitation normal values\".\n", "instrument": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-gridded,surface-observations,weather", "license": "proprietary", "missionStartDate": "1901-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA US Gridded Climate Normals (Cloud-Optimized GeoTIFF)"}, "noaa-climate-normals-netcdf": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThe data in this Collection are the original NetCDF files provided by NOAA's National Centers for Environmental Information. This Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nFor most use-cases, we recommend using the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded) collection, which contains the same data in Cloud Optimized GeoTIFF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-netcdf,surface-observations,weather", "license": "proprietary", "missionStartDate": "1901-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA US Gridded Climate Normals (NetCDF)"}, "noaa-climate-normals-tabular": {"abstract": "The [NOAA United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals) provide information about typical climate conditions for thousands of weather station locations across the United States. Normals act both as a ruler to compare current weather and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables for each weather station. \n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains tabular weather variable data at weather station locations in GeoParquet format, converted from the source CSV files. The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nData are provided for annual/seasonal, monthly, daily, and hourly frequencies for the following time periods:\n\n- Legacy 30-year normals (1981\u20132010)\n- Supplemental 15-year normals (2006\u20132020)\n", "instrument": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-tabular,surface-observations,weather", "license": "proprietary", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA US Tabular Climate Normals"}, "noaa-mrms-qpe-1h-pass1": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the **1-Hour Pass 1** sub-product, i.e., 1-hour cumulative precipitation accumulation with a 1-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass1,precipitation,qpe,united-states,weather", "license": "proprietary", "missionStartDate": "2022-07-21T20:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA MRMS QPE 1-Hour Pass 1"}, "noaa-mrms-qpe-1h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the **1-Hour Pass 2** sub-product, i.e., 1-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "missionStartDate": "2022-07-21T20:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA MRMS QPE 1-Hour Pass 2"}, "noaa-mrms-qpe-24h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the **24-Hour Pass 2** sub-product, i.e., 24-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-24h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "missionStartDate": "2022-07-21T20:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA MRMS QPE 24-Hour Pass 2"}, "noaa-nclimgrid-monthly": {"abstract": "The [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) consists of four climate variables derived from the [Global Historical Climatology Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) dataset: maximum temperature, minimum temperature, average temperature, and precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer) grids for the Continental United States (CONUS). \n\nNClimGrid data is available in monthly and daily temporal intervals, with the daily data further differentiated as \"prelim\" (preliminary) or \"scaled\". Preliminary daily data is available within approximately three days of collection. Once a calendar month of preliminary daily data has been collected, it is scaled to match the corresponding monthly value. Monthly data is available from 1895 to the present. Daily preliminary and daily scaled data is available from 1951 to the present. \n\nThis Collection contains **Monthly** data. See the journal publication [\"Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml) for more information about monthly gridded data.\n\nUsers of all NClimGrid data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) that:\n>\"On an annual basis, approximately one year of 'final' NClimGrid data is submitted to replace the initially supplied 'preliminary' data for the same time period. Users should be sure to ascertain which level of data is required for their research.\"\n\nThe source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n*Note*: The Planetary Computer currently has STAC metadata for just the monthly collection. We'll have STAC metadata for daily data in our next release. In the meantime, you can access the daily NetCDF data directly from Blob Storage using the storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`. See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns for more.*\n", "instrument": null, "keywords": "climate,nclimgrid,noaa,noaa-nclimgrid-monthly,precipitation,temperature,united-states", "license": "proprietary", "missionStartDate": "1895-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Monthly NOAA U.S. Climate Gridded Dataset (NClimGrid)"}, "nrcan-landcover": {"abstract": "Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis.", "instrument": null, "keywords": "canada,land-cover,landsat,north-america,nrcan-landcover,remote-sensing", "license": "OGL-Canada-2.0", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Land Cover of Canada"}, "planet-nicfi-analytic": {"abstract": "*Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "keywords": "imagery,nicfi,planet,planet-nicfi-analytic,satellite,tropics", "license": "proprietary", "missionStartDate": "2015-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Planet-NICFI Basemaps (Analytic)"}, "planet-nicfi-visual": {"abstract": "*Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "keywords": "imagery,nicfi,planet,planet-nicfi-visual,satellite,tropics", "license": "proprietary", "missionStartDate": "2015-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Planet-NICFI Basemaps (Visual)"}, "sentinel-1-grd": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Level-1 Ground Range Detected (GRD) products in this Collection consist of focused SAR data that has been detected, multi-looked and projected to ground range using the Earth ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range (but can be different for each IW/EW sub-swath).\n\nGround range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution.\n\nFor the IW and EW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarization.\n\nFor more information see the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\n\n### Terrain Correction\n\nUsers might want to geometrically or radiometrically terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc) collection is a global radiometrically terrain corrected dataset derived from Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM). See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/) for more.", "instrument": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "missionStartDate": "2014-10-10T00:28:21Z", "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "title": "Sentinel 1 Level-1 Ground Range Detected (GRD)"}, "sentinel-1-rtc": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd) products produced by the European Space Agency. The RTC processing is performed by [Catalyst](https://catalyst.earth/).\n\nRadiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.\n\nA Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed) for more information.\n\n### Methodology\n\nThe Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/S1-Radiometric-Calibration-V1.0.pdf). The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.\n\nThe data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, [\u201cFlattening Gamma: Radiometric Terrain Correction for SAR Imagery\u201d](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf), IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.\n\nCalibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.\n\nRTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.\n", "instrument": null, "keywords": "c-band,copernicus,esa,rtc,sar,sentinel,sentinel-1,sentinel-1-rtc,sentinel-1a,sentinel-1b", "license": "CC-BY-4.0", "missionStartDate": "2014-10-10T00:28:21Z", "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "title": "Sentinel 1 Radiometrically Terrain Corrected (RTC)"}, "sentinel-2-l2a": {"abstract": "The [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using [Sen2Cor](https://step.esa.int/main/snap-supported-plugins/sen2cor/) and converted to [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": "msi", "keywords": "copernicus,esa,global,imagery,msi,reflectance,satellite,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31Z", "platform": "sentinel-2", "platformSerialIdentifier": "Sentinel-2A,Sentinel-2B", "processingLevel": null, "title": "Sentinel-2 Level-2A"}, "sentinel-3-olci-lfr-l2-netcdf": {"abstract": "This collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2] products containing data on global vegetation, chlorophyll, and water vapor.\n\n## Data files\n\nThis dataset includes data on three primary variables:\n\n* OLCI global vegetation index file\n* terrestrial Chlorophyll index file\n* integrated water vapor over water file.\n\nEach variable is contained within a separate NetCDF file, and is cataloged as an asset in each Item.\n\nSeveral associated variables are also provided in the annotations data files:\n\n* rectified reflectance for red and NIR channels (RC681 and RC865)\n* classification, quality and science flags (LQSF)\n* common data such as the ortho-geolocation of land pixels, solar and satellite angles, atmospheric and meteorological data, time stamp or instrument information. These variables are inherited from Level-1B products.\n\nThis full resolution product offers a spatial sampling of approximately 300 m.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/land-products\n", "instrument": "OLCI", "keywords": "biomass,copernicus,esa,land,olci,sentinel,sentinel-3,sentinel-3-olci-lfr-l2-netcdf,sentinel-3a,sentinel-3b", "license": "proprietary", "missionStartDate": "2016-04-25T11:33:47.368562Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land (Full Resolution)"}, "sentinel-3-olci-wfr-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 Full Resolution [OLCI Level-2 Water][olci-l2] products containing data on water-leaving reflectance, ocean color, and more.\n\n## Data files\n\nThis dataset includes data on:\n\n- Surface directional reflectance\n- Chlorophyll-a concentration\n- Suspended matter concentration\n- Energy flux\n- Aerosol load\n- Integrated water vapor column\n\nEach variable is contained within NetCDF files. Error estimates are available for each product.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from November 2017 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/ocean-products\n", "instrument": "OLCI", "keywords": "copernicus,esa,ocean,olci,sentinel,sentinel-3,sentinel-3-olci-wfr-l2-netcdf,sentinel-3a,sentinel-3b,water", "license": "proprietary", "missionStartDate": "2017-11-01T00:07:01.738487Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Water (Full Resolution)"}, "sentinel-3-slstr-frp-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Fire Radiative Power](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) (FRP) products containing data on fires detected over land and ocean.\n\n## Data files\n\nThe primary measurement data is contained in the `FRP_in.nc` file and provides FRP and uncertainties, projected onto a 1km grid, for fires detected in the thermal infrared (TIR) spectrum over land. Since February 2022, FRP and uncertainties are also provided for fires detected in the short wave infrared (SWIR) spectrum over both land and ocean, with the delivered data projected onto a 500m grid. The latter SWIR-detected fire data is only available for night-time measurements and is contained in the `FRP_an.nc` or `FRP_bn.nc` files.\n\nIn addition to the measurement data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags.\n\n## Processing\n\nThe TIR fire detection is based on measurements from the S7 and F1 bands of the [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument); SWIR fire detection is based on the S5 and S6 bands. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\n\nThis Collection contains Level-2 data in NetCDF files from August 2020 to present.\n", "instrument": "SLSTR", "keywords": "copernicus,esa,fire,satellite,sentinel,sentinel-3,sentinel-3-slstr-frp-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "missionStartDate": "2020-08-08T23:11:15.617203Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Fire Radiative Power"}, "sentinel-3-slstr-lst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Land Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) products containing data on land surface temperature measurements on a 1km grid. Radiance is measured in two channels to determine the temperature of the Earth's surface skin in the instrument field of view, where the term \"skin\" refers to the top surface of bare soil or the effective emitting temperature of vegetation canopies as viewed from above.\n\n## Data files\n\nThe dataset includes data on the primary measurement variable, land surface temperature, in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains several ancillary variables:\n\n- Normalized Difference Vegetation Index\n- Surface biome classification\n- Fractional vegetation cover\n- Total water vapor column\n\nIn addition to the primary and ancillary data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n## STAC Item geometries\n\nThe Collection contains small \"chips\" and long \"stripes\" of data collected along the satellite direction of travel. Approximately five percent of the STAC Items describing long stripes of data contain geometries that encompass a larger area than an exact concave hull of the data extents. This may require additional filtering when searching the Collection for Items that spatially intersect an area of interest.\n", "instrument": "SLSTR", "keywords": "copernicus,esa,land,satellite,sentinel,sentinel-3,sentinel-3-slstr-lst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "missionStartDate": "2016-04-19T01:35:17.188500Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land Surface Temperature"}, "sentinel-3-slstr-wst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Water Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) products containing data on sea surface temperature measurements on a 1km grid. Each product consists of a single NetCDF file containing all data variables:\n\n- Sea Surface Temperature (SST) value\n- SST total uncertainty\n- Latitude and longitude coordinates\n- SST time deviation\n- Single Sensor Error Statistic (SSES) bias and standard deviation estimate\n- Contextual parameters such as wind speed at 10 m and fractional sea-ice contamination\n- Quality flag\n- Satellite zenith angle\n- Top Of Atmosphere (TOA) Brightness Temperature (BT)\n- TOA noise equivalent BT\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from October 2017 to present.\n", "instrument": "SLSTR", "keywords": "copernicus,esa,ocean,satellite,sentinel,sentinel-3,sentinel-3-slstr-wst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "missionStartDate": "2017-10-31T23:59:57.451604Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Sea Surface Temperature"}, "sentinel-3-sral-lan-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Land Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on land radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from March 2016 to present.\n", "instrument": "SRAL", "keywords": "altimetry,copernicus,esa,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-lan-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "missionStartDate": "2016-03-01T14:07:51.632846Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land Radar Altimetry"}, "sentinel-3-sral-wat-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on ocean radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from January 2017 to present.\n", "instrument": "SRAL", "keywords": "altimetry,copernicus,esa,ocean,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-wat-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "missionStartDate": "2017-01-28T00:59:14.149496Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Ocean Radar Altimetry"}, "sentinel-3-synergy-aod-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Aerosol Optical Depth](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) product, which is a downstream development of the Sentinel-2 Level-1 [OLCI Full Resolution](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci/data-formats/level-1) and [SLSTR Radiances and Brightness Temperatures](https://sentinels.copernicus.eu/web/sentinel/user-guides/Sentinel-3-slstr/data-formats/level-1) products. The dataset provides both retrieved and diagnostic global aerosol parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF file for all regions over land and ocean free of snow/ice cover, excluding high cloud fraction data. The retrieved and derived aerosol parameters are:\n\n- Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\n- Error estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and 2250 nm\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and 2250 nm\n- Fine-mode AOD at 550nm\n- Aerosol Angstrom parameter between 550 and 865nm\n- Dust AOD at 550nm\n- Aerosol absorption optical depth at 550nm\n\nAtmospherically corrected nadir surface directional reflectances at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution are also provided. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\n\nThis Collection contains Level-2 data in NetCDF files from April 2020 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "aerosol,copernicus,esa,global,olci,satellite,sentinel,sentinel-3,sentinel-3-synergy-aod-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2020-04-16T19:36:28.012367Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Global Aerosol"}, "sentinel-3-synergy-syn-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) product, which contains data on Surface Directional Reflectance, Aerosol Optical Thickness, and an Angstrom coefficient estimate over land.\n\n## Data Files\n\nIndividual NetCDF files for the following variables:\n\n- Surface Directional Reflectance (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr) channels (S1 to S6 for both nadir and oblique views, except S4) and for all [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci) channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the water vapor bands Oa19 and Oa20.\n- Aerosol optical thickness at 550nm with error estimates.\n- Angstrom coefficient at 550nm.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "aerosol,copernicus,esa,land,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-syn-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-09-22T16:51:00.001276Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land Surface Reflectance and Aerosol"}, "sentinel-3-synergy-v10-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from ground reflectance during a 10-day window, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 10-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-v10-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-09-27T11:17:21Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 10-Day Surface Reflectance and NDVI (SPOT VEGETATION)"}, "sentinel-3-synergy-vg1-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 1-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from daily ground reflecrtance, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 1-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vg1-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-10-04T23:17:21Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 1-Day Surface Reflectance and NDVI (SPOT VEGETATION)"}, "sentinel-3-synergy-vgp-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Top of Atmosphere Reflectance](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) product, which is a SPOT VEGETATION Continuity Product containing measurement data similar to that obtained by the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboad the SPOT-3 and SPOT-4 satellites. The primary variables are four top of atmosphere reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument and have been adapted for scientific applications requiring highly accurate physical measurements through correction for systematic errors and re-sampling to predefined geographic projections. The pixel brightness count is the ground area's apparent reflectance as seen at the top of atmosphere.\n\n## Data files\n\nNetCDF files are provided for the four reflectance bands. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "copernicus,esa,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vgp-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-10-08T08:09:40.491227Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Top of Atmosphere Reflectance (SPOT VEGETATION)"}, "sentinel-5p-l2-netcdf": {"abstract": "The Copernicus [Sentinel-5 Precursor](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p) mission provides high spatio-temporal resolution measurements of the Earth's atmosphere. The mission consists of one satellite carrying the [TROPOspheric Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html) spacecraft, allowing utilization of co-located cloud mask data provided by the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging) (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane product.\n\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5) mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products) provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone and cloud & aerosol information. These measurements are used for improving air quality forecasts and monitoring the concentrations of atmospheric constituents.\n\nThis STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format, since April 2018 for the following products:\n\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index): Ultraviolet aerosol index\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height): Aerosol layer height\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane): Methane (CH4) total column\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud): Cloud fraction, albedo, and top pressure\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide): Carbon monoxide (CO) total column\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde): Formaldehyde (HCHO) total column\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide): Nitrogen dioxide (NO2) total column\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column): Ozone (O3) total column\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column): Ozone (O3) tropospheric column\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide): Sulfur dioxide (SO2) total column\n* [`L2__NP_BD3`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 3\n* [`L2__NP_BD6`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 6\n* [`L2__NP_BD7`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 7\n", "instrument": "TROPOMI", "keywords": "air-quality,climate-change,copernicus,esa,forecasting,sentinel,sentinel-5-precursor,sentinel-5p,sentinel-5p-l2-netcdf,tropomi", "license": "proprietary", "missionStartDate": "2018-04-30T00:18:50Z", "platform": "Sentinel-5P", "platformSerialIdentifier": "Sentinel 5 Precursor", "processingLevel": null, "title": "Sentinel-5P Level-2"}, "terraclimate": {"abstract": "[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958 to the present. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. This dataset is provided in [Zarr](https://zarr.readthedocs.io/) format.\n", "instrument": null, "keywords": "climate,precipitation,temperature,terraclimate,vapor-pressure,water", "license": "CC0-1.0", "missionStartDate": "1958-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TerraClimate"}, "us-census": {"abstract": "The [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) counted every person living in the United States and the five U.S. territories. It marked the 24th census in U.S. history and the first time that households were invited to respond to the census online.\n\nThe tables included on the Planetary Computer provide information on population and geographic boundaries at various levels of cartographic aggregation.\n", "instrument": null, "keywords": "administrative-boundaries,demographics,population,us-census,us-census-bureau", "license": "proprietary", "missionStartDate": "2021-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "US Census"}, "usda-cdl": {"abstract": "The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission \"to provide timely, accurate and useful statistics in service to U.S. agriculture\" (Johnson and Mueller, 2010, p. 1204). The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season.\n\nThis collection includes Cropland, Confidence, Cultivated, and Frequency products.\n\n- Cropland: Crop-specific land cover data created annually. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.\n- Confidence: The predicted confidence associated with an output pixel. A value of zero indicates low confidence, while a value of 100 indicates high confidence.\n- Cultivated: cultivated and non-cultivated land cover for CONUS based on land cover information derived from the 2017 through 2021 Cropland products.\n- Frequency: crop specific planting frequency based on land cover information derived from the 2008 through 2021 Cropland products.\n\nFor more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).", "instrument": null, "keywords": "agriculture,land-cover,land-use,united-states,usda,usda-cdl", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USDA Cropland Data Layers (CDLs)"}, "usgs-lcmap-conus-v13": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products), which was released in August 2022 for years 1985-2021. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "keywords": "conus,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-conus-v13", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS LCMAP CONUS Collection 1.3"}, "usgs-lcmap-hawaii-v10": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products), which was released in January 2022 for years 2000-2020. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "keywords": "hawaii,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-hawaii-v10", "license": "proprietary", "missionStartDate": "2000-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS LCMAP Hawaii Collection 1.0"}}, "providers_config": {"3dep-lidar-classification": {"productType": "3dep-lidar-classification"}, "3dep-lidar-copc": {"productType": "3dep-lidar-copc"}, "3dep-lidar-dsm": {"productType": "3dep-lidar-dsm"}, "3dep-lidar-dtm": {"productType": "3dep-lidar-dtm"}, "3dep-lidar-dtm-native": {"productType": "3dep-lidar-dtm-native"}, "3dep-lidar-hag": {"productType": "3dep-lidar-hag"}, "3dep-lidar-intensity": {"productType": "3dep-lidar-intensity"}, "3dep-lidar-pointsourceid": {"productType": "3dep-lidar-pointsourceid"}, "3dep-lidar-returns": {"productType": "3dep-lidar-returns"}, "3dep-seamless": {"productType": "3dep-seamless"}, "alos-dem": {"productType": "alos-dem"}, "alos-fnf-mosaic": {"productType": "alos-fnf-mosaic"}, "alos-palsar-mosaic": {"productType": "alos-palsar-mosaic"}, "aster-l1t": {"productType": "aster-l1t"}, "chesapeake-lc-13": {"productType": "chesapeake-lc-13"}, "chesapeake-lc-7": {"productType": "chesapeake-lc-7"}, "chesapeake-lu": {"productType": "chesapeake-lu"}, "chloris-biomass": {"productType": "chloris-biomass"}, "cil-gdpcir-cc-by": {"productType": "cil-gdpcir-cc-by"}, "cil-gdpcir-cc-by-sa": {"productType": "cil-gdpcir-cc-by-sa"}, "cil-gdpcir-cc0": {"productType": "cil-gdpcir-cc0"}, "conus404": {"productType": "conus404"}, "cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "daymet-annual-hi": {"productType": "daymet-annual-hi"}, "daymet-annual-na": {"productType": "daymet-annual-na"}, "daymet-annual-pr": {"productType": "daymet-annual-pr"}, "daymet-daily-hi": {"productType": "daymet-daily-hi"}, "daymet-daily-na": {"productType": "daymet-daily-na"}, "daymet-daily-pr": {"productType": "daymet-daily-pr"}, "daymet-monthly-hi": {"productType": "daymet-monthly-hi"}, "daymet-monthly-na": {"productType": "daymet-monthly-na"}, "daymet-monthly-pr": {"productType": "daymet-monthly-pr"}, "deltares-floods": {"productType": "deltares-floods"}, "deltares-water-availability": {"productType": "deltares-water-availability"}, "drcog-lulc": {"productType": "drcog-lulc"}, "eclipse": {"productType": "eclipse"}, "ecmwf-forecast": {"productType": "ecmwf-forecast"}, "era5-pds": {"productType": "era5-pds"}, "esa-cci-lc": {"productType": "esa-cci-lc"}, "esa-cci-lc-netcdf": {"productType": "esa-cci-lc-netcdf"}, "esa-worldcover": {"productType": "esa-worldcover"}, "fia": {"productType": "fia"}, "fws-nwi": {"productType": "fws-nwi"}, "gap": {"productType": "gap"}, "gbif": {"productType": "gbif"}, "gnatsgo-rasters": {"productType": "gnatsgo-rasters"}, "gnatsgo-tables": {"productType": "gnatsgo-tables"}, "goes-cmi": {"productType": "goes-cmi"}, "goes-glm": {"productType": "goes-glm"}, "gpm-imerg-hhr": {"productType": "gpm-imerg-hhr"}, "gridmet": {"productType": "gridmet"}, "hgb": {"productType": "hgb"}, "hrea": {"productType": "hrea"}, "io-biodiversity": {"productType": "io-biodiversity"}, "io-lulc": {"productType": "io-lulc"}, "io-lulc-9-class": {"productType": "io-lulc-9-class"}, "io-lulc-annual-v02": {"productType": "io-lulc-annual-v02"}, "jrc-gsw": {"productType": "jrc-gsw"}, "kaza-hydroforecast": {"productType": "kaza-hydroforecast"}, "landsat-c2-l1": {"productType": "landsat-c2-l1"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "mobi": {"productType": "mobi"}, "modis-09A1-061": {"productType": "modis-09A1-061"}, "modis-09Q1-061": {"productType": "modis-09Q1-061"}, "modis-10A1-061": {"productType": "modis-10A1-061"}, "modis-10A2-061": {"productType": "modis-10A2-061"}, "modis-11A1-061": {"productType": "modis-11A1-061"}, "modis-11A2-061": {"productType": "modis-11A2-061"}, "modis-13A1-061": {"productType": "modis-13A1-061"}, "modis-13Q1-061": {"productType": "modis-13Q1-061"}, "modis-14A1-061": {"productType": "modis-14A1-061"}, "modis-14A2-061": {"productType": "modis-14A2-061"}, "modis-15A2H-061": {"productType": "modis-15A2H-061"}, "modis-15A3H-061": {"productType": "modis-15A3H-061"}, "modis-16A3GF-061": {"productType": "modis-16A3GF-061"}, "modis-17A2H-061": {"productType": "modis-17A2H-061"}, "modis-17A2HGF-061": {"productType": "modis-17A2HGF-061"}, "modis-17A3HGF-061": {"productType": "modis-17A3HGF-061"}, "modis-21A2-061": {"productType": "modis-21A2-061"}, "modis-43A4-061": {"productType": "modis-43A4-061"}, "modis-64A1-061": {"productType": "modis-64A1-061"}, "ms-buildings": {"productType": "ms-buildings"}, "mtbs": {"productType": "mtbs"}, "naip": {"productType": "naip"}, "nasa-nex-gddp-cmip6": {"productType": "nasa-nex-gddp-cmip6"}, "nasadem": {"productType": "nasadem"}, "noaa-c-cap": {"productType": "noaa-c-cap"}, "noaa-cdr-ocean-heat-content": {"productType": "noaa-cdr-ocean-heat-content"}, "noaa-cdr-ocean-heat-content-netcdf": {"productType": "noaa-cdr-ocean-heat-content-netcdf"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"productType": "noaa-cdr-sea-surface-temperature-optimum-interpolation"}, "noaa-cdr-sea-surface-temperature-whoi": {"productType": "noaa-cdr-sea-surface-temperature-whoi"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"productType": "noaa-cdr-sea-surface-temperature-whoi-netcdf"}, "noaa-climate-normals-gridded": {"productType": "noaa-climate-normals-gridded"}, "noaa-climate-normals-netcdf": {"productType": "noaa-climate-normals-netcdf"}, "noaa-climate-normals-tabular": {"productType": "noaa-climate-normals-tabular"}, "noaa-mrms-qpe-1h-pass1": {"productType": "noaa-mrms-qpe-1h-pass1"}, "noaa-mrms-qpe-1h-pass2": {"productType": "noaa-mrms-qpe-1h-pass2"}, "noaa-mrms-qpe-24h-pass2": {"productType": "noaa-mrms-qpe-24h-pass2"}, "noaa-nclimgrid-monthly": {"productType": "noaa-nclimgrid-monthly"}, "nrcan-landcover": {"productType": "nrcan-landcover"}, "planet-nicfi-analytic": {"productType": "planet-nicfi-analytic"}, "planet-nicfi-visual": {"productType": "planet-nicfi-visual"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "sentinel-1-rtc": {"productType": "sentinel-1-rtc"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "sentinel-3-olci-lfr-l2-netcdf": {"productType": "sentinel-3-olci-lfr-l2-netcdf"}, "sentinel-3-olci-wfr-l2-netcdf": {"productType": "sentinel-3-olci-wfr-l2-netcdf"}, "sentinel-3-slstr-frp-l2-netcdf": {"productType": "sentinel-3-slstr-frp-l2-netcdf"}, "sentinel-3-slstr-lst-l2-netcdf": {"productType": "sentinel-3-slstr-lst-l2-netcdf"}, "sentinel-3-slstr-wst-l2-netcdf": {"productType": "sentinel-3-slstr-wst-l2-netcdf"}, "sentinel-3-sral-lan-l2-netcdf": {"productType": "sentinel-3-sral-lan-l2-netcdf"}, "sentinel-3-sral-wat-l2-netcdf": {"productType": "sentinel-3-sral-wat-l2-netcdf"}, "sentinel-3-synergy-aod-l2-netcdf": {"productType": "sentinel-3-synergy-aod-l2-netcdf"}, "sentinel-3-synergy-syn-l2-netcdf": {"productType": "sentinel-3-synergy-syn-l2-netcdf"}, "sentinel-3-synergy-v10-l2-netcdf": {"productType": "sentinel-3-synergy-v10-l2-netcdf"}, "sentinel-3-synergy-vg1-l2-netcdf": {"productType": "sentinel-3-synergy-vg1-l2-netcdf"}, "sentinel-3-synergy-vgp-l2-netcdf": {"productType": "sentinel-3-synergy-vgp-l2-netcdf"}, "sentinel-5p-l2-netcdf": {"productType": "sentinel-5p-l2-netcdf"}, "terraclimate": {"productType": "terraclimate"}, "us-census": {"productType": "us-census"}, "usda-cdl": {"productType": "usda-cdl"}, "usgs-lcmap-conus-v13": {"productType": "usgs-lcmap-conus-v13"}, "usgs-lcmap-hawaii-v10": {"productType": "usgs-lcmap-hawaii-v10"}}}, "usgs_satapi_aws": {"product_types_config": {"landsat-c2ard-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere Brightness Temperature (BT) Product"}, "landsat-c2ard-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Reflectance (SR) Product"}, "landsat-c2ard-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Temperature (ST) Product"}, "landsat-c2ard-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere (TA) Reflectance Product"}, "landsat-c2l1": {"abstract": "The Landsat Level-1 product is a top of atmosphere product distributed as scaled and calibrated digital numbers.", "instrument": null, "keywords": "landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l1", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1972-07-25T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_1,LANDSAT_2,LANDSAT_3,LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-1 Product"}, "landsat-c2l2-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 UTM Surface Reflectance (SR) Product"}, "landsat-c2l2-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 UTM Surface Temperature (ST) Product"}, "landsat-c2l2alb-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere Brightness Temperature (BT) Product"}, "landsat-c2l2alb-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Surface Reflectance (SR) Product"}, "landsat-c2l2alb-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Surface Temperature (ST) Product"}, "landsat-c2l2alb-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product"}, "landsat-c2l3-ba": {"abstract": "The Landsat Burned Area (BA) contains two acquisition-based raster data products that represent burn classification and burn probability.", "instrument": null, "keywords": "analysis-ready-data,burned-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-ba", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-3 Burned Area (BA) Product"}, "landsat-c2l3-dswe": {"abstract": "The Landsat Dynamic Surface Water Extent (DSWE) product contains six acquisition-based raster data products pertaining to the existence and condition of surface water.", "instrument": null, "keywords": "analysis-ready-data,dynamic-surface-water-extent-,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-dswe", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-3 Dynamic Surface Water Extent (DSWE) Product"}, "landsat-c2l3-fsca": {"abstract": "The Landsat Fractional Snow Covered Area (fSCA) product contains an acquisition-based per-pixel snow cover fraction, an acquisition-based revised cloud mask for quality assessment, and a product metadata file.", "instrument": null, "keywords": "analysis-ready-data,fractional-snow-covered-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-fsca", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-3 Fractional Snow Covered Area (fSCA) Product"}}, "providers_config": {"landsat-c2ard-bt": {"productType": "landsat-c2ard-bt"}, "landsat-c2ard-sr": {"productType": "landsat-c2ard-sr"}, "landsat-c2ard-st": {"productType": "landsat-c2ard-st"}, "landsat-c2ard-ta": {"productType": "landsat-c2ard-ta"}, "landsat-c2l1": {"productType": "landsat-c2l1"}, "landsat-c2l2-sr": {"productType": "landsat-c2l2-sr"}, "landsat-c2l2-st": {"productType": "landsat-c2l2-st"}, "landsat-c2l2alb-bt": {"productType": "landsat-c2l2alb-bt"}, "landsat-c2l2alb-sr": {"productType": "landsat-c2l2alb-sr"}, "landsat-c2l2alb-st": {"productType": "landsat-c2l2alb-st"}, "landsat-c2l2alb-ta": {"productType": "landsat-c2l2alb-ta"}, "landsat-c2l3-ba": {"productType": "landsat-c2l3-ba"}, "landsat-c2l3-dswe": {"productType": "landsat-c2l3-dswe"}, "landsat-c2l3-fsca": {"productType": "landsat-c2l3-fsca"}}}, "wekeo_cmems": {"product_types_config": {"EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1D-m_202105": {"abstract": "'''Short description:'''\nThe operational TOPAZ5-ECOSMO Arctic Ocean system uses the ECOSMO biological model coupled online to the TOPAZ5 physical model planned for a future update of the ARCTIC_ANALYSIS_FORECAST_PHYS_002_001_a physical forecast. It is run daily to provide 10 days of forecast of 3D biogeochemical variables ocean. The coupling is done by the FABM framework.\n\nCoupling to a biological ocean model provides a description of the evolution of basic biogeochemical variables. The output consists of daily mean fields interpolated onto a standard grid and 40 fixed levels in NetCDF4 CF format. Variables include 3D fields of nutrients (nitrate, phosphate, silicate), phytoplankton and zooplankton biomass, oxygen, chlorophyll, primary productivity, carbon cycle variables (pH, dissolved inorganic carbon and surface partial CO2 pressure in seawater, carbon export) and light attenuation coefficient. Surface Chlorophyll-a from satellite ocean colour is assimilated every week and projected downwards using the Uitz et al. (2006) method. A new 10-day forecast is produced daily using the previous day's forecast and the most up-to-date prognostic forcing fields.\nOutput products have 6.25 km resolution at the North Pole (equivalent to 1/8 deg) on a stereographic projection. See the Product User Manual for the exact projection parameters.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00003", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-bgc-002-004:cmems-mod-arc-bgc-anfc-ecosmo-p1d-m-202105,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,sea-water-ph-reported-on-total-scale,sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211": {"abstract": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-bgc-002-004:cmems-mod-arc-bgc-anfc-ecosmo-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,sea-water-ph-reported-on-total-scale,sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1D-m_202311": {"abstract": "'''Short description:'''\n\nThe operational TOPAZ5 Arctic Ocean system uses the HYCOM model and a 100-member EnKF assimilation scheme. It is run daily to provide 10 days of forecast (average of 10 members) of the 3D physical ocean, including sea ice with the CICEv5.1 model; data assimilation is performed weekly to provide 7 days of analysis (ensemble average).\n\nOutput products are interpolated on a grid of 6 km resolution at the North Pole on a polar stereographic projection. The geographical projection follows these proj4 library parameters: \n\nproj4 = \"+units=m +proj=stere +lon_0=-45 +lat_0=90 +k=1 +R=6378273 +no_defs\" \n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00001", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-002-001:cmems-mod-arc-phy-anfc-6km-detided-p1d-m-202311,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311": {"abstract": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-002-001:cmems-mod-arc-phy-anfc-6km-detided-p1m-m-202311,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011:cmems_mod_arc_phy_anfc_nextsim_P1M-m_202311": {"abstract": "'''Short Description:'''\n\nThe Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, including the Canadian Archipelago, the Baffin and Hudson Bays. neXtSIM is forced with surface atmosphere forcings from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ5, the ARC MFC PHY NRT system (002_001a). neXtSIM runs daily, assimilating manual ice charts, sea ice thickness from CS2SMOS in winter and providing 9-day forecasts. The output variables are the ice concentrations, ice thickness, ice drift velocity, snow depths, sea ice type, sea ice age, ridge volume fraction and albedo, provided at hourly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n\n'''DOI (product) :''' \n\nhttps://doi.org/10.48670/moi-00004", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-ice-002-011:cmems-mod-arc-phy-anfc-nextsim-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,surface-snow-thickness,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-11-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1D-m_202105": {"abstract": "'''Short description:'''\n\nThe TOPAZ-ECOSMO reanalysis system assimilates satellite chlorophyll observations and in situ nutrient profiles. The model uses the Hybrid Coordinate Ocean Model (HYCOM) coupled online to a sea ice model and the ECOSMO biogeochemical model. It uses the Determinstic version of the Ensemble Kalman Smoother to assimilate remotely sensed colour data and nutrient profiles. Data assimilation, including the 80-member ensemble production, is performed every 8-days. Atmospheric forcing fields from the ECMWF ERA-5 dataset are used.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00006", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-bgc-002-005:cmems-mod-arc-bgc-my-ecosmo-p1d-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-bgc-002-005:cmems-mod-arc-bgc-my-ecosmo-p1m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-bgc-002-005:cmems-mod-arc-bgc-my-ecosmo-p1y-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-hflux-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-hflux-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-mflux-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-mflux-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-topaz4-p1d-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1M_202012": {"abstract": "'''Short description:'''\n\nThe current version of the TOPAZ system - TOPAZ4b - is nearly identical to the real-time forecast system run at MET Norway. It uses a recent version of the Hybrid Coordinate Ocean Model (HYCOM) developed at University of Miami (Bleck 2002). HYCOM is coupled to a sea ice model; ice thermodynamics are described in Drange and Simonsen (1996) and the elastic-viscous-plastic rheology in Hunke and Dukowicz (1997). The model's native grid covers the Arctic and North Atlantic Oceans, has fairly homogeneous horizontal spacing (between 11 and 16 km). 50 hybrid layers are used in the vertical (z-isopycnal), more than the TOPAZ4 system (28 layers). TOPAZ4b uses the Deterministic version of the Ensemble Kalman filter (DEnKF; Sakov and Oke 2008) to assimilate remotely sensed as well as temperature and salinity profiles. The output is interpolated onto standard grids and depths. Daily values are provided at all depths. Data assimilation, including the 100-member ensemble production, is performed weekly.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00007", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-topaz4-p1m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-topaz4-p1y-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411", "instrument": null, "keywords": "eo:mo:dat:arctic-multiyear-phy-ice-002-016:cmems-mod-arc-phy-my-nextsim-p1d-m-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411", "instrument": null, "keywords": "eo:mo:dat:arctic-multiyear-phy-ice-002-016:cmems-mod-arc-phy-my-nextsim-p1m-m-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_7-10days_P1D-i_202411": {"abstract": "'''Short description:'''\n\nThis Baltic Sea biogeochemical model product provides forecasts for the biogeochemical conditions in the Baltic Sea. The Baltic forecast is updated daily providing a new six days forecast. Three different datasets are provided. One with daily means and one with monthly means values for these parameters: nitrate, phosphate, chl-a, ammonium, dissolved oxygen, ph, phytoplankton, zooplankton, silicate, dissolved inorganic carbon, and partial pressure of co2 at the surface. Instantaenous values for the Secchi Depth and light attenuation valid for noon (12Z) are included in the daily mean files/dataset. Additionally a third dataset with daily accumulated values of the netto primary production is available. The product is produced by the biogeochemical model ERGOM (Neumann, 2000) one way coupled to a Baltic Sea set up of the NEMO ocean model, which provides the CMEMS Baltic physical ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). This biogeochemical product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and up to 56 vertical depth levels. The product covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00009", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-pp-anfc-7-10days-p1d-i-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-pp-anfc-p1d-i-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-anfc-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-anfc-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-anfc-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-cur-anfc-detided-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-cur-anfc-detided-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-ssh-anfc-detided-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-7-10days-pt15m-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-7-10days-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1M-m_202311": {"abstract": "'''Short description:'''\n\nThis Baltic Sea physical model product provides forecasts for the physical conditions in the Baltic Sea. The Baltic forecast is updated twice daily providing a new six days forecast. Several datasets are provided: One with hourly instantaneous values, one with daily mean values and one with monthly mean values, all containing these parameters: sea level variations, ice concentration and thickness at the surface, and temperature, salinity and horizontal and vertical velocities for the 3D field. Additionally a dataset with 15 minutes (instantaneous) surface values are provided for the sea level variation and the surface horizontal currents. The product is produced by a Baltic Sea set up of the NEMOv4.0 ocean model. This product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and up to 56 vertical depth levels. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The ocean model is forced with Stokes drift data from the Baltic Wave forecast product (BALTICSEA_ANALYSISFORECAST_WAV_003_010). Satellite SST, ice concentrations and in-situ T and S profiles are assimilated into the model's analysis field. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00010", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-pt15m-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-wav-003-010:cmems-mod-bal-wav-anfc-7-10days-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_PT1H-i_202311": {"abstract": "'''Short description:'''\n\nThis Baltic Sea wave model product provides forecasts for the wave conditions in the Baltic Sea. The Baltic forecast is updated twice a day providing a new six days forecast with hourly instantaneous data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the Stokes drift, and two paramters for the maximum wave. The product is based on the wave model WAM cycle 4.7. The wave model is forced with surface currents, sea level anomaly and ice information from the CMEMS BAL MFC ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00011", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-wav-003-010:cmems-mod-bal-wav-anfc-pt1h-i-202311,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-bgc-003-012:cmems-mod-bal-bgc-my-p1d-m-202303,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-bgc-003-012:cmems-mod-bal-bgc-my-p1m-m-202303,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1Y-m_202303": {"abstract": "'''Short description:'''\n\nThis Baltic Sea Biogeochemical Reanalysis product provides a biogeochemical reanalysis for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the biogeochemical model ERGOM one-way online-coupled with the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include nitrate, phosphate, ammonium, dissolved oxygen, ph, chlorophyll-a, secchi depth, surface partial co2 pressure and net primary production. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n'''DOI (product) :'''\n\nhttps://doi.org/10.48670/moi-00012", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-bgc-003-012:cmems-mod-bal-bgc-my-p1y-m-202303,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1D-m_202303": {"abstract": "'''Short description:'''\n\nThis Baltic Sea Physical Reanalysis product provides a reanalysis for the physical conditions for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include sea level, ice concentration, ice thickness, salinity, temperature, horizonal velocities and the mixed layer depths. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00013", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:balticsea-multiyear-phy-003-011:cmems-mod-bal-phy-my-p1d-m-202303,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:balticsea-multiyear-phy-003-011:cmems-mod-bal-phy-my-p1m-m-202303,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:balticsea-multiyear-phy-003-011:cmems-mod-bal-phy-my-p1y-m-202303,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411", "instrument": null, "keywords": "eo:mo:dat:balticsea-multiyear-wav-003-015:cmems-mod-bal-wav-my-2km-climatology-p1m-m-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411", "instrument": null, "keywords": "eo:mo:dat:balticsea-multiyear-wav-003-015:cmems-mod-bal-wav-my-pt1h-i-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411", "instrument": null, "keywords": "eo:mo:dat:balticsea-multiyear-wav-003-015:cmems-mod-bal-wav-my-aflux-pt1h-i-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-bio-anfc-3km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-bio-anfc-3km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-car-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-car-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-car-anfc-3km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-car-anfc-3km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-2.5km-pt1h-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-3km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-3km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_PT1H-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_PT1H-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-3km-pt1h-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-nut-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-nut-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-nut-anfc-3km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-nut-anfc-3km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1D-m_202311": {"abstract": "'''Short description:''' \n\nBLKSEA_ANALYSISFORECAST_BGC_007_010 is the nominal product of the Black Sea Biogeochemistry NRT system and is generated by the NEMO 4.0-BAMHBI modelling system. Biogeochemical Model for Hypoxic and Benthic Influenced areas (BAMHBI) is an innovative biogeochemical model with a 28-variable pelagic component (including the carbonate system) and a 6-variable benthic component ; it explicitely represents processes in the anoxic layer.\nThe product provides analysis and forecast for 3D concentration of chlorophyll, nutrients (nitrate and phosphate), dissolved oxygen, phytoplankton carbon biomass, net primary production, pH, dissolved inorganic carbon, total alkalinity, and for 2D fields of bottom oxygen concentration (for the North-Western shelf), surface partial pressure of CO2 and surface flux of CO2. These variables are computed on a grid with ~3km x 59-levels resolution, and are provided as daily and monthly means.\n\n'''Product Citation:''' \n\nPlease refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (product) :''' \nhttps://doi.org/10.25423/cmcc/blksea_analysisforecast_bgc_007_010", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-opt-anfc-3km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-opt-anfc-3km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-optics-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-optics-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pft-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pft-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pft-anfc-3km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pft-anfc-3km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pp-o2-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pp-o2-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-detided-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-detided-2.5km-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i_202311": {"abstract": "'''Short description''': \n\nThe BLKSEA_ANALYSISFORECAST_PHY_007_001 is produced with a hydrodynamic model implemented over the whole Black Sea basin, including the Bosporus Strait and a portion of the Marmara Sea for the optimal interface with the Mediterranean Sea through lateral open boundary conditions. The model horizontal grid resolution is 1/40\u00b0 in zonal and 1/40\u00b0 in meridional direction (ca. 121 km) and has 121 unevenly spaced vertical levels. The product provides analysis and forecast for 3D potential temperature, salinity, horizontal and vertical currents. Together with the 2D variables sea surface height, bottom potential temperature and mixed layer thickness.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/cmcc/blksea_analysisforecast_phy_007_001_eas6", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-mld-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-mld-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-mld-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-detided-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-detided-2.5km-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-2.5km-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-2.5km-p1m-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_PT1H-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_PT1H-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-2.5km-pt1h-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-temp-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-temp-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-temp-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_WAV_007_003:cmems_mod_blk_wav_anfc_2.5km_PT1H-i_202411": {"abstract": "'''Short description''': \n\nThe wave analysis and forecasts for the Black Sea are produced with the third generation spectral wave model WAM Cycle 6. The hindcast and ten days forecast are produced twice a day on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74518. The model takes into account depth refraction, wave breaking, and assimilation of satellite wave and wind data. The system provides a hindcast and ten days forecast with one-hourly output twice a day. The atmospheric forcing is taken from ECMWF analyses and forecast data. Additionally, WAM is forced by surface currents and sea surface height from BLKSEA_ANALYSISFORECAST_PHY_007_001. Monthly statistics are provided operationally on the Product Quality Dashboard following the CMEMS metrics definitions.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:blksea-analysisforecast-wav-007-003:cmems-mod-blk-wav-anfc-2.5km-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-maximum-period,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1D-m_202311": {"abstract": "'''Short description:''' \n\nThe biogeochemical reanalysis for the Black Sea is produced by the MAST/ULiege Production Unit by means of the BAMHBI biogeochemical model. The workflow runs on the CECI hpc infrastructure (Wallonia, Belgium).\n\n''Product Citation'': Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n''DOI (product)'': https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m_202411": {"abstract": "'''Short description''': \n\nThe BLKSEA_MULTIYEAR_PHY_007_004 product provides monthly and daily ocean fields for the Black Sea basin starting from 01/01/1993. The hydrodynamical core is based on NEMO general circulation ocean model, implemented in the BS domain with horizontal resolution of 1/27\u00b0 x 1/36\u00b0 and 31 vertical levels. NEMO is forced by atmospheric surface fluxes computed by bulk formulation using ECMWF ERA5 atmospheric fields at the resolution of 0.25\u00b0 in space and 1-h in time. The current version has closed boundary at the Bosporus Strait. The model is online coupled to OceanVar assimilation scheme to assimilate sea level anomaly along-track observations from CMEMS and available in situ vertical profiles of temperature and salinity from both SeaDataNet and CMEMS datasets. \n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-hflux-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-hflux-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mflux-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mflux-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-wflux-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-wflux-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-aflux-my-2.5km-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m_202311": {"abstract": "'''Short description''': \nThe wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74,518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/cmcc/blksea_multiyear_wav_007_006_eas4", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-my-2.5km-climatology-pt1m-m-202311,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-my-2.5km-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-myint-2.5km-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-bio-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-bio-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-car-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-car-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-co2-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-co2-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-nut-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-nut-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-optics-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m_202311": {"abstract": "'''Short description:'''\n\nThe Operational Mercator Ocean biogeochemical global ocean analysis and forecast system at 1/4 degree is providing 10 days of 3D global ocean forecasts updated weekly. The time series is aggregated in time, in order to reach a two full year\u2019s time series sliding window. This product includes daily and monthly mean files of biogeochemical parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production, phytoplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5700 meters.\n\n* NEMO version (v3.6_STABLE)\n* Forcings: GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 at daily frequency. \n* Outputs mean fields are interpolated on a standard regular grid in NetCDF format.\n* Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID[http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-028.pdf] \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00015", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-optics-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-pft-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-pft-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-plankton-anfc-0.25deg-p1d-m-202411,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-plankton-anfc-0.25deg-p1m-m-202411,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-cur-anfc-0.083deg-p1d-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m_202406": {"abstract": "'''Short description'''\n\nThe Operational Mercator global ocean analysis and forecast system at 1/12 degree is providing 10 days of 3D global ocean forecasts updated daily. The time series is aggregated in time in order to reach a two full year\u2019s time series sliding window.\n\nThis product includes daily and monthly mean files of temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom over the global ocean. It also includes hourly mean surface fields for sea level height, temperature and currents. The global ocean output files are displayed with a 1/12 degree horizontal resolution with regular longitude/latitude equirectangular projection.\n\n50 vertical levels are ranging from 0 to 5500 meters.\n\nThis product also delivers a special dataset for surface current which also includes wave and tidal drift called SMOC (Surface merged Ocean Current).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00016", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-cur-anfc-0.083deg-p1m-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-cur-anfc-0.083deg-pt6h-i-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-so-anfc-0.083deg-p1d-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-so-anfc-0.083deg-p1m-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-so-anfc-0.083deg-pt6h-i-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-thetao-anfc-0.083deg-p1d-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-thetao-anfc-0.083deg-p1m-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-thetao-anfc-0.083deg-pt6h-i-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-wcur-anfc-0.083deg-p1d-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-wcur-anfc-0.083deg-p1m-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-climatology-uncertainty-p1m-m-202311,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-sst-anomaly-p1d-m-202411,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-sst-anomaly-p1m-m-202411,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-p1d-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-p1m-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-pt1h-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-merged-sl-pt1h-i-202411,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-merged-uv-pt1h-i-202211,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_WAV_001_027:cmems_mod_glo_wav_anfc_0.083deg_PT3H-i_202411": {"abstract": "'''Short description:'''\n \nThe operational global ocean analysis and forecast system of M\u00e9t\u00e9o-France with a resolution of 1/12 degree is providing daily analyses and 10 days forecasts for the global ocean sea surface waves. This product includes 3-hourly instantaneous fields of integrated wave parameters from the total spectrum (significant height, period, direction, Stokes drift,...etc), as well as the following partitions: the wind wave, the primary and secondary swell waves.\n \nThe global wave system of M\u00e9t\u00e9o-France is based on the wave model MFWAM which is a third generation wave model. MFWAM uses the computing code ECWAM-IFS-38R2 with a dissipation terms developed by Ardhuin et al. (2010). The model MFWAM was upgraded on november 2014 thanks to improvements obtained from the european research project \u00ab my wave \u00bb (Janssen et al. 2014). The model mean bathymetry is generated by using 2-minute gridded global topography data ETOPO2/NOAA. Native model grid is irregular with decreasing distance in the latitudinal direction close to the poles. At the equator the distance in the latitudinal direction is more or less fixed with grid size 1/10\u00b0. The operational model MFWAM is driven by 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF atmospheric system. The wave spectrum is discretized in 24 directions and 30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM uses the assimilation of altimeters with a time step of 6 hours. The global wave system provides analysis 4 times a day, and a forecast of 10 days at 0:00 UTC. The wave model MFWAM uses the partitioning to split the swell spectrum in primary and secondary swells.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00017", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-analysisforecast-wav-001-027:cmems-mod-glo-wav-anfc-0.083deg-pt3h-i-202411,forecast,global-ocean,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-my-0.25deg-p1d-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-my-0.25deg-p1m-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1D-m_202406": {"abstract": "'''Short description'''\n\nThe biogeochemical hindcast for global ocean is produced at Mercator-Ocean (Toulouse. France). It provides 3D biogeochemical fields since year 1993 at 1/4 degree and on 75 vertical levels. It uses PISCES biogeochemical model (available on the NEMO[https://www.nemo-ocean.eu/] modelling platform). No data assimilation in this product.\n\n* Latest NEMO version (v3.6_STABLE)\n* Forcings: FREEGLORYS2V4[https://www.mercator-ocean.fr/en/solutions-expertise/how-to-access-the-mercator-ocean-services/let-s-define-your-needs/] ocean physics produced at Mercator-Ocean and ERA-Interim[https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim] atmosphere produced at ECMWF at a daily frequency \n* Outputs: Daily (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production) and monthly (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production. iron. phytoplankton in carbon) 3D mean fields interpolated on a standard regular grid in NetCDF format. The simulation is performed once and for all.\n* Initial conditions: World Ocean Atlas 2013 for nitrate. phosphate. silicate and dissolved oxygen. GLODAPv2 for DIC and Alkalinity. and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID[http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-029.pdf]\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00019", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-myint-0.25deg-p1d-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-myint-0.25deg-p1m-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i_202411": {"abstract": "'''Short description:'''\nThe Low and Mid-Trophic Levels (LMTL) reanalysis for global ocean is produced at [https://www.cls.fr CLS] on behalf of Global Ocean Marine Forecasting Center. It provides 2D fields of biomass content of zooplankton and six functional groups of micronekton. It uses the LMTL component of SEAPODYM dynamical population model (http://www.seapodym.eu). No data assimilation has been done. This product also contains forcing data: net primary production, euphotic depth, depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers.\n\n'''Forcings sources:'''\n* Ocean currents and temperature (CMEMS multiyear product)\n* Net Primary Production computed from chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high latitudes (CMEMS biogeochemistry multiyear product)\n\n'''Vertical coverage:'''\n* Epipelagic layer \n* Upper mesopelagic layer\n* Lower mesopelagic layer (max. 1000m)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00020", "instrument": null, "keywords": "/biological-oceanography/phytoplankton-and-microphytobenthos,/biological-oceanography/zooplankton,atlantic-ocean,coastal-marine-environment,data,eastward-sea-water-velocity-vertical-mean-over-pelagic-layer,eo:mo:dat:global-multiyear-bgc-001-033:cmems-mod-glo-bgc-my-0.083deg-lmtl-fphy-pt1d-i-202411,euphotic-zone-depth,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-zooplankton-expressed-as-carbon-in-sea-water,modelling-data,multi-year,net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water,north-mid-atlantic-ridge,northward-sea-water-velocity-vertical-mean-over-pelagic-layer,not-applicable,numerical-model,oceanographic-geographical-features,sea-water-pelagic-layer-bottom-depth,sea-water-potential-temperature-vertical-mean-over-pelagic-layer,weather-climate-and-seasonal-forecasting,wp3-pelagic-mapping", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411", "instrument": null, "keywords": "/biological-oceanography/phytoplankton-and-microphytobenthos,/biological-oceanography/zooplankton,atlantic-ocean,coastal-marine-environment,data,eastward-sea-water-velocity-vertical-mean-over-pelagic-layer,eo:mo:dat:global-multiyear-bgc-001-033:cmems-mod-glo-bgc-my-0.083deg-lmtl-pt1d-i-202411,euphotic-zone-depth,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-zooplankton-expressed-as-carbon-in-sea-water,modelling-data,multi-year,net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water,north-mid-atlantic-ridge,northward-sea-water-velocity-vertical-mean-over-pelagic-layer,not-applicable,numerical-model,oceanographic-geographical-features,sea-water-pelagic-layer-bottom-depth,sea-water-potential-temperature-vertical-mean-over-pelagic-layer,weather-climate-and-seasonal-forecasting,wp3-pelagic-mapping", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-my-0.083deg-climatology-p1m-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-my-0.083deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1M-m_202311": {"abstract": "'''Short description:'''\n \nThe GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12\u00b0 horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward).\n\nIt is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.\n\nThis product includes daily and monthly mean files for temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom. The global ocean output files are displayed on a standard regular grid at 1/12\u00b0 (approximatively 8 km) and on 50 standard levels.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00021", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-my-0.083deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-myint-0.083deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-myint-0.083deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-all-my-0.25deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-all-my-0.25deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-mnstd-my-0.25deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m_202311": {"abstract": "'''Short description:'''\n\n You can find here the CMEMS Global Ocean Ensemble Reanalysis product at \u00bc degree resolution : monthly means of Temperature, Salinity, Currents and Ice variables for 75 vertical levels, starting from 1993 onward.\n \nGlobal ocean reanalyses are homogeneous 3D gridded descriptions of the physical state of the ocean covering several decades, produced with a numerical ocean model constrained with data assimilation of satellite and in situ observations. These reanalyses are built to be as close as possible to the observations (i.e. realistic) and in agreement with the model physics The multi-model ensemble approach allows uncertainties or error bars in the ocean state to be estimated.\n\nThe ensemble mean may even provide for certain regions and/or periods a more reliable estimate than any individual reanalysis product.\n\nThe four reanalyses, used to create the ensemble, covering \u201caltimetric era\u201d period (starting from 1st of January 1993) during which altimeter altimetry data observations are available:\n * GLORYS2V4 from Mercator Ocean (Fr);\n * ORAS5 from ECMWF;\n * GloSea5 from Met Office (UK);\n * and C-GLORSv7 from CMCC (It);\n \nThese four products provided four different time series of global ocean simulations 3D monthly estimates. All numerical products available for users are monthly or daily mean averages describing the ocean.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00024", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-mnstd-my-0.25deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-multiyear-wav-001-032:cmems-mod-glo-wav-my-0.2deg-climatology-p1m-m-202311,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-multiyear-wav-001-032:cmems-mod-glo-wav-my-0.2deg-pt3h-i-202411,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_myint_0.2deg_PT3H-i_202311": {"abstract": "'''Short description:'''\n\nGLOBAL_REANALYSIS_WAV_001_032 for the global wave reanalysis describing past sea states since years 1993. This product also bears the name of WAVERYS within the GLO-HR MFC. for correspondence to other global multi-year products like GLORYS. BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5\u00b0 irregular grid. Average wave quantities derived from this wave spectrum. such as the SWH (significant wave height) or the average wave period. are delivered on a regular 1/5\u00b0 grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from a geometric sequence of first member 0.035 Hz and a reason 7.5. WAVERYS takes into account oceanic currents from the GLORYS12 physical ocean reanalysis and assimilates significant wave height observed from historical altimetry missions and directional wave spectra from Sentinel 1 SAR from 2017 onwards. \n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00022", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-multiyear-wav-001-032:cmems-mod-glo-wav-myint-0.2deg-pt3h-i-202311,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Reanalysis"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1D-m_202411": {"abstract": "'''Short description:'''\nThe IBI-MFC provides a high-resolution biogeochemical analysis and forecast product covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as daily averaged forecasts with a horizon of 10 days (updated on a weekly basis) are available on the catalogue.\nTo this aim, an online coupled physical-biogeochemical operational system is based on NEMO-PISCES at 1/36\u00b0 and adapted to the IBI area, being Mercator-Ocean in charge of the model code development. PISCES is a model of intermediate complexity, with 24 prognostic variables. It simulates marine biological productivity of the lower trophic levels and describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe).\nThe product provides daily and monthly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, surface partial pressure of carbon dioxide, and zooplankton.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00026", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-optics-anfc-0.027deg-p1d-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-optics-anfc-0.027deg-p1m-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-anfc-0.027deg-3d-p1d-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-anfc-0.027deg-3d-p1m-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "\"''Short description:'''\nThe IBI-MFC provides a high-resolution ocean analysis and forecast product (daily run by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as forecasts of different temporal resolutions with a horizon of 5 days (updated on a daily basis) are available on the catalogue.\nThe system is based on a eddy-resolving NEMO model application at 1/36\u00ba horizontal resolution, being Mercator-Ocean in charge of the model code development. The hydrodynamic forecast includes high frequency processes of paramount importance to characterize regional scale marine processes: tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge, wave forcing in forecast, etc. A weekly update of IBI downscaled analysis is also delivered as historic IBI best estimates.\nThe product offers 3D daily and monthly ocean fields, as well as hourly mean and 15-minute instantaneous values for some surface variables. Daily and monthly averages of 3D Temperature, 3D Salinity, 3D Zonal and Meridional Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are delivered. Finally, 15-minute instantaneous values of Sea Surface Height and Currents are also given.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00027", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-cur-anfc-detided-0.027deg-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-cur-anfc-detided-0.027deg-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-ssh-anfc-detided-0.027deg-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-ssh-anfc-detided-0.027deg-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-wcur-anfc-0.027deg-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-wcur-anfc-0.027deg-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-2d-pt15m-i-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-2d-pt1h-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-3d-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-3d-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-3d-pt1h-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-analysisforecast-wav-005-005:cmems-mod-ibi-wav-anfc-0.027deg-pt1h-i-202411,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),wave-spectra,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.05deg_PT1H-i_202311": {"abstract": "'''Short description:'''\nThe IBI-MFC provides a high-resolution wave analysis and forecast product (run twice a day by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates), as well as hourly instantaneous forecasts with a horizon of up to 10 days (updated on a daily basis) are available on the catalogue.\nThe IBI wave model system is based on the MFWAM model and runs on a grid of 5 km of horizontal resolution forced with the ECMWF hourly wind data. The system assimilates significant wave height (SWH) altimeter data and CFOSAT wave spectral data (supplied by M\u00e9t\u00e9o-France), and it is forced by currents provided by the IBI ocean circulation system. \nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Additionally, the IBI wave system is set up to provide internally some key parameters adequate to be used as forcing in the IBI NEMO ocean model forecast run.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00025", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-analysisforecast-wav-005-005:cmems-mod-ibi-wav-anfc-0.05deg-pt1h-i-202311,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),wave-spectra,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-my-0.083deg-p1d-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-my-0.083deg-p1m-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-my-0.083deg-p1y-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-climatology-p1m-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-p1d-m-202012,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-p1m-m-202012,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m_202211": {"abstract": "'''Short description:'''\nThe IBI-MFC provides a biogeochemical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources.\nTo this aim, an application of the biogeochemical model PISCES is run simultaneously with the ocean physical IBI reanalysis, generating both products at the same 1/12\u00b0 horizontal resolution. The PISCES model is able to simulate the first levels of the marine food web, from nutrients up to mesozooplankton and it has 24 state variables.\nThe product provides daily, monthly and yearly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon and surface partial pressure of carbon dioxide. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00028", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-p1y-m-202211,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-hflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-hflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-mflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-mflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wcur-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wcur-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wcur-0.083deg-p1y-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-2d-pt1h-m-202012,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-climatology-p1m-m-202211,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-p1d-m-202012,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-p1m-m-202012,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m_202211": {"abstract": "'''Short description:'''\nThe IBI-MFC provides a ocean physical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources. \nThe IBI model numerical core is based on the NEMO v3.6 ocean general circulation model run at 1/12\u00b0 horizontal resolution. Altimeter data, in situ temperature and salinity vertical profiles and satellite sea surface temperature are assimilated.\nThe product offers 3D daily, monthly and yearly ocean fields, as well as hourly mean fields for surface variables. Daily, monthly and yearly averages of 3D Temperature, 3D Salinity, 3D Zonal and Meridional Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are distributed. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00029", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-p1y-m-202211,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-multiyear-wav-005-006:cmems-mod-ibi-wav-my-aflux-0.027deg-p1h-i-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m_202311": {"abstract": "'''Short description:'''\nThe IBI-MFC provides a high-resolution wave reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly extended on a yearly basis. The model system is run by Nologin with the support of CESGA in terms of supercomputing resources. \nThe Multi-Year model configuration is based on the MFWAM model developed by M\u00e9t\u00e9o-France (MF), covering the same region as the IBI-MFC Near Real Time (NRT) analysis and forecasting product, but with an enhanced horizontal resolution (1/36\u00ba instead of 1/20\u00ba). The system assimilates significant wave height (SWH) altimeter data and wave spectral data (Envisat and CFOSAT), supplied by MF. Both, the MY and the NRT products, are fed by ECMWF hourly winds. Specifically, the MY system is forced by the ERA5 reanalysis wind data. As boundary conditions, the NRT system uses the 2D wave spectra from the Copernicus Marine GLOBAL forecast system, whereas the MY system is nested to the GLOBAL reanalysis.\nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Additionally, climatological parameters of significant wave height (VHM0) and zero -crossing wave period (VTM02) are delivered for the time interval 1993-2016.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00030", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-multiyear-wav-005-006:cmems-mod-ibi-wav-my-0.027deg-climatology-p1m-m-202311,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-multiyear-wav-005-006:cmems-mod-ibi-wav-my-0.027deg-pt1h-i-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "EO:MO:DAT:INSITU_GLO_BGC_DISCRETE_MY_013_046:cmems_obs-ins_glo_bgc-nut_my_na_irr_202411": {"abstract": "'''Short description:'''\nFor the Global Ocean- In-situ observation delivered in delayed mode. This In Situ delayed mode product integrates the best available version of in situ oxygen, chlorophyll / fluorescence and nutrients data.\n\n'''DOI (product) :''' \nhttps://doi.org/10.17882/86207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-bgc-discrete-my-013-046:cmems-obs-ins-glo-bgc-nut-my-na-irr-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-silicate-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Delayed Mode Biogeochemical product"}, "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_PT1H_202411": {"abstract": "'''Short description:'''\n\nThis product integrates sea level observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from the Global telecommunication system (GTS) used by the Met Offices.\n\n'''DOI (product) :''' \nhttps://doi.org/10.17882/93670", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ssh-discrete-my-013-053:cmems-obs-ins-glo-phy-ssh-my-na-pt1h-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,near-real-time,non-tidal-elevation-of-sea-surface-height,not-applicable,oceanographic-geographical-features,tidal-sea-surface-height-above-reference-datum,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Delayed Mode Sea level product"}, "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_irr_202411": {"abstract": "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_irr_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ssh-discrete-my-013-053:cmems-obs-ins-glo-phy-ssh-my-na-irr-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,near-real-time,non-tidal-elevation-of-sea-surface-height,not-applicable,oceanographic-geographical-features,tidal-sea-surface-height-above-reference-datum,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Delayed Mode Sea level product"}, "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_ibi_phy-ssh_my_tide-surge_PT1H_202411": {"abstract": "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_ibi_phy-ssh_my_tide-surge_PT1H_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ssh-discrete-my-013-053:cmems-obs-ins-ibi-phy-ssh-my-tide-surge-pt1h-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,near-real-time,non-tidal-elevation-of-sea-surface-height,not-applicable,oceanographic-geographical-features,tidal-sea-surface-height-above-reference-datum,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Delayed Mode Sea level product"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_MY_013_052:cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M_202411": {"abstract": "'''Short description:''''\nGlobal Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the reprocessed in-situ global product CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) using the ISAS software. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n'''DOI (product) :''' \nhttps://doi.org/10.17882/46219", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ts-oa-my-013-052:cmems-obs-ins-glo-phy-temp-sal-my-cora-oa-p1m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1960-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_NRT_013_002:cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M_202411": {"abstract": "'''Short description:'''\nFor the Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the in-situ near real time database are produced monthly. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a support for localized experience (cruises), providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00037", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ts-oa-nrt-013-002:cmems-obs-ins-glo-phy-temp-sal-nrt-oa-p1m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2015-01-15", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean- Real time in-situ observations objective analysis"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_adcp_irr_202411": {"abstract": "\"'Short description: '''\n\nGlobal Ocean - This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface and subsurface currents. Current data from 4 different types of instruments are distributed: \n* The NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML) Surface Velocity Program (SVP) Drifter\u2019s reprocessing from 1990. It provides the drifter's position, velocity and includes temperature measurements. In addition, a wind slippage correction is provided from 1993. \n* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency (HF) radars that are part of the European HF radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata. \n* The zonal and meridional velocities, at parking depth (mostly around 1000m) and at the surface, calculated along the trajectories of the floats which are part of the Argo Program. \n* The velocity profiles within the water column coming from Acoustic Doppler Current Profiler (vessel mounted ADCP, Moored ADCP, saildrones) platforms\n\n'''DOI (product) :'''\nhttps://doi.org/10.17882/86236", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:insitu-glo-phy-uv-discrete-my-013-044:cmems-obs-ins-glo-phy-cur-my-adcp-irr-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,not-applicable,oceanographic-geographical-features,sea-water-temperature,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-30", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_argo_irr_202411": {"abstract": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_argo_irr_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:insitu-glo-phy-uv-discrete-my-013-044:cmems-obs-ins-glo-phy-cur-my-argo-irr-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,not-applicable,oceanographic-geographical-features,sea-water-temperature,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-30", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_drifter_PT1H_202411": {"abstract": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_drifter_PT1H_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:insitu-glo-phy-uv-discrete-my-013-044:cmems-obs-ins-glo-phy-cur-my-drifter-pt1h-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,not-applicable,oceanographic-geographical-features,sea-water-temperature,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-30", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_glider_irr_202411": {"abstract": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_glider_irr_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:insitu-glo-phy-uv-discrete-my-013-044:cmems-obs-ins-glo-phy-cur-my-glider-irr-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,not-applicable,oceanographic-geographical-features,sea-water-temperature,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-30", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_PT1H_202411": {"abstract": "'''Short description:'''\n\nThese products integrate wave observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from National Data Centers (NODCs) and JCOMM global systems (OceanSITES, DBCP) and the Global telecommunication system (GTS) used by the Met Offices.\n\n'''DOI (product) :''' \nhttps://doi.org/10.17882/70345", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-wav-discrete-my-013-045:cmems-obs-ins-glo-wav-my-na-pt1h-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,sea-surface-wave-mean-period,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-30", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Delayed Mode Wave product"}, "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_irr_202411": {"abstract": "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_irr_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-wav-discrete-my-013-045:cmems-obs-ins-glo-wav-my-na-irr-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,sea-surface-wave-mean-period,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-30", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Delayed Mode Wave product"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-bio-anfc-4.2km-p1d-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-bio-anfc-4.2km-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-car-anfc-4.2km-p1d-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-car-anfc-4.2km-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-co2-anfc-4.2km-p1d-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-co2-anfc-4.2km-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-nut-anfc-4.2km-p1d-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-nut-anfc-4.2km-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202211": {"abstract": "'''Short Description'''\nThe biogeochemical analysis and forecasts for the Mediterranean Sea at 1/24\u00b0 of horizontal resolution (ca. 4 km) are produced by means of the MedBFM4 model system. MedBFM4, which is run by OGS (IT), consists of the coupling of the multi-stream atmosphere radiative model OASIM, the multi-stream in-water radiative and tracer transport model OGSTM_BIOPTIMOD v4.3, and the biogeochemical flux model BFM v5. Additionally, MedBFM4 features the 3D variational data assimilation scheme 3DVAR-BIO v3.3 with the assimilation of surface chlorophyll (CMEMS-OCTAC NRT product) and of vertical profiles of chlorophyll, nitrate and oxygen (BGC-Argo floats provided by CORIOLIS DAC).\nThe biogeochemical MedBFM system, which is forced by the NEMO-OceanVar model (MEDSEA_ANALYSIS_FORECAST_PHY_006_013 product run by CMCC), produces one day of hindcast and ten days of forecast (every day) and seven days of analysis (weekly on Tuesday).\n\nSalon, S., Cossarini, G., Bolzon, G., Feudale, L., Lazzari, P., Teruzzi, A., Solidoro, C., Crise, A., 2019. Marine Ecosystem forecasts: skill performance of the CMEMS Mediterranean Sea model system. Ocean Sci. Discuss. 1\u201335. https://doi.org/10.5194/os-2018-145\n\n''Product Citation'': Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n''DOI (Product)'': https://doi.org/10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbfm4", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-optics-anfc-4.2km-p1d-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-optics-anfc-4.2km-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202311": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202311", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-pft-anfc-4.2km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202311": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202311", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-pft-anfc-4.2km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "'''Short Description'''\nThe physical component of the Mediterranean Forecasting System (Med-Physics) is a coupled hydrodynamic-wave model implemented over the whole Mediterranean Basin including tides. The model horizontal grid resolution is 1/24\u02da (ca. 4 km) and has 141 unevenly spaced vertical levels.\nThe hydrodynamics are supplied by the Nucleous for European Modelling of the Ocean NEMO (v4.2) and include the representation of tides, while the wave component is provided by Wave Watch-III (v6.07) coupled through OASIS; the model solutions are corrected by a 3DVAR assimilation scheme (OceanVar) for temperature and salinity vertical profiles and along track satellite Sea Level Anomaly observations.\n\n''Product Citation'': Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n''DOI (Product)'': https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-detided-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-mld-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-mld-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-mld-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-detided-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-wcur-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-wcur-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_WAV_006_017:cmems_mod_med_wav_anfc_4.2km_PT1H-i_202311": {"abstract": "'''Short description:'''\n \nMEDSEA_ANALYSISFORECAST_WAV_006_017 is the nominal wave product of the Mediterranean Sea Forecasting system, composed by hourly wave parameters at 1/24\u00ba horizontal resolution covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The waves forecast component (Med-WAV system) is a wave model based on the WAM Cycle 6. The Med-WAV modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins and the model solutions are corrected by an optimal interpolation data assimilation scheme of all available along track satellite significant wave height observations. The atmospheric forcing is provided by the operational ECMWF Numerical Weather Prediction model and the wave model is forced with hourly averaged surface currents and sea level obtained from MEDSEA_ANALYSISFORECAST_PHY_006_013 at 1/24\u00b0 resolution. The model uses wave spectra for Open Boundary Conditions from GLOBAL_ANALYSIS_FORECAST_WAV_001_027 product. The wave system includes 2 forecast cycles providing twice per day a Mediterranean wave analysis and 10 days of wave forecasts.\n\n''Product Citation'': Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (product)''': https://doi.org/10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-wav-006-017:cmems-mod-med-wav-anfc-4.2km-pt1h-i-202311,forecast,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-bio-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-bio-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-car-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_myint_4.2km_P1M-m_202112": {"abstract": "'''Short Description'''\nThe Mediterranean Sea biogeochemical reanalysis at 1/24\u00b0 of horizontal resolution (ca. 4 km) covers the period from Jan 1999 to 1 month to the present and is produced by means of the MedBFM3 model system. MedBFM3, which is run by OGS (IT), includes the transport model OGSTM v4.0 coupled with the biogeochemical flux model BFM v5 and the variational data assimilation module 3DVAR-BIO v2.1 for surface chlorophyll. MedBFM3 is forced by the physical reanalysis (MEDSEA_MULTIYEAR_PHY_006_004 product run by CMCC) that provides daily forcing fields (i.e., currents, temperature, salinity, diffusivities, wind and solar radiation). The ESA-CCI database of surface chlorophyll concentration (CMEMS-OCTAC REP product) is assimilated with a weekly frequency. \n\nCossarini, G., Feudale, L., Teruzzi, A., Bolzon, G., Coidessa, G., Solidoro C., Amadio, C., Lazzari, P., Brosich, A., Di Biagio, V., and Salon, S., 2021. High-resolution reanalysis of the Mediterranean Sea biogeochemistry (1999-2019). Frontiers in Marine Science. Front. Mar. Sci. 8:741486.doi: 10.3389/fmars.2021.741486\n\n''Product Citation'': Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n''DOI (Product)'': https://doi.org/10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3\n\n''DOI (Interim dataset)'':\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-car-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-co2-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-co2-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-nut-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-nut-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-pft-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-plankton-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-bio-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-bio-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-car-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-car-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-co2-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-co2-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-nut-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-nut-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-pft-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-pft-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-cur-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-hflux-my-4.2km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-hflux-my-4.2km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-mflux-my-4.2km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-mflux-my-4.2km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-mld-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-sal-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-ssh-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-tem-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-wflux-my-4.2km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-wflux-my-4.2km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-my-4.2km-climatology-p1m-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-h_202012": {"abstract": "'''Short description:'''\n\nThe Med MFC physical multiyear product is generated by a numerical system composed of an hydrodynamic model, supplied by the Nucleous for European Modelling of the Ocean (NEMO) and a variational data assimilation scheme (OceanVAR) for temperature and salinity vertical profiles and satellite Sea Level Anomaly along track data. It contains a reanalysis dataset and an interim dataset which covers the period after the reanalysis until 1 month before present. The model horizontal grid resolution is 1/24\u02da (ca. 4-5 km) and the unevenly spaced vertical levels are 141. \n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n\n'''DOI (Interim dataset)''':\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-rean-h-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-mld-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-mld-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-mld-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-sal-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-sal-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-sal-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-rean-h-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-tem-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-tem-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-tem-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_my_4.2km-climatology_P1M-m_202311": {"abstract": "'''Short description:'''\n\nMEDSEA_MULTIYEAR_WAV_006_012 is the multi-year wave product of the Mediterranean Sea Waves forecasting system (Med-WAV). It contains a Reanalysis dataset, an Interim dataset covering the period after the reanalysis until 1 month before present and a monthly climatological dataset (reference period 1993-2016). The Reanalysis dataset is a multi-year wave reanalysis starting from January 1993, composed by hourly wave parameters at 1/24\u00b0 horizontal resolution, covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The Med-WAV modelling system is based on wave model WAM 4.6.2 and has been developed as a nested sequence of two computational grids (coarse and fine) to ensure that swell propagating from the North Atlantic (NA) towards the strait of Gibraltar is correctly entering the Mediterranean Sea. The coarse grid covers the North Atlantic Ocean from 75\u00b0W to 10\u00b0E and from 70\u00b0 N to 10\u00b0 S in 1/6\u00b0 resolution while the nested fine grid covers the Mediterranean Sea from 18.125\u00b0 W to 36.2917\u00b0 E and from 30.1875\u00b0 N to 45.9792\u00b0 N with a 1/24\u00b0 resolution. The modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins. The wave system also includes an optimal interpolation assimilation scheme assimilating significant wave height along track satellite observations available through CMEMS and it is forced with daily averaged currents from Med-Physics and with 1-h, 0.25\u00b0 horizontal-resolution ERA5 reanalysis 10m-above-sea-surface winds from ECMWF.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (product)''': \nhttps://doi.org/10.25423/cmcc/medsea_multiyear_wav_006_012\n\n'''DOI (Interim dataset)''':\nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-multiyear-wav-006-012:cmems-mod-med-wav-my-4.2km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-multiyear-wav-006-012:cmems-mod-med-wav-myint-4.2km-pt1h-i-202112,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-multiyear-wav-006-012:med-hcmr-wav-rean-h-202411,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Reanalysis"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg-climatology_P1M-m_202411": {"abstract": "'''Short description:'''\n\nThis product consists of 3D fields of Particulate Organic Carbon (POC), Particulate Backscattering coefficient (bbp) and Chlorophyll-a concentration (Chla) at depth. The reprocessed product is provided at 0.25\u00b0x0.25\u00b0 horizontal resolution, over 36 levels from the surface to 1000 m depth. \nA neural network method estimates both the vertical distribution of Chla concentration and of particulate backscattering coefficient (bbp), a bio-optical proxy for POC, from merged surface ocean color satellite measurements with hydrological properties and additional relevant drivers. \n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00046\n\n'''Product Citation:''' \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:multiobs-glo-bio-bgc-3d-rep-015-010:cmems-obs-mob-glo-bgc-chl-poc-my-0.25deg-climatology-p1m-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,modelling-data,multi-year,none,oceanographic-geographical-features,satellite-observation,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:multiobs-glo-bio-bgc-3d-rep-015-010:cmems-obs-mob-glo-bgc-chl-poc-my-0.25deg-p7d-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,modelling-data,multi-year,none,oceanographic-geographical-features,satellite-observation,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411", "instrument": null, "keywords": "eo:mo:dat:multiobs-glo-bio-carbon-surface-mynrt-015-008:cmems-obs-mob-glo-bgc-car-my-irr-i-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411", "instrument": null, "keywords": "eo:mo:dat:multiobs-glo-bio-carbon-surface-mynrt-015-008:cmems-obs-mob-glo-bgc-car-nrt-irr-i-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThis product is a L4 REP and NRT global total velocity field at 0m and 15m together wiht its individual components (geostrophy and Ekman) and related uncertainties. It consists of the zonal and meridional velocity at a 1h frequency and at 1/4 degree regular grid. The total velocity fields are obtained by combining CMEMS satellite Geostrophic surface currents and modelled Ekman currents at the surface and 15m depth (using ERA5 wind stress in REP and ERA5* in NRT). 1 hourly product, daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program).\n\n'''Product Citation:'''\nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00327", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eastward-sea-water-velocity-due-to-ekman-drift-standard-error,eastward-sea-water-velocity-standard-error,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-my-0.25deg-p1d-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,northward-sea-water-velocity-due-to-ekman-drift-standard-error,northward-sea-water-velocity-standard-error,numerical-model,oceanographic-geographical-features,satellite-observation,surface-geostophic-eastward-sea-water-velocity-standard-error,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eastward-sea-water-velocity-due-to-ekman-drift-standard-error,eastward-sea-water-velocity-standard-error,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-my-0.25deg-p1m-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,northward-sea-water-velocity-due-to-ekman-drift-standard-error,northward-sea-water-velocity-standard-error,numerical-model,oceanographic-geographical-features,satellite-observation,surface-geostophic-eastward-sea-water-velocity-standard-error,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eastward-sea-water-velocity-due-to-ekman-drift-standard-error,eastward-sea-water-velocity-standard-error,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-my-0.25deg-pt1h-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,northward-sea-water-velocity-due-to-ekman-drift-standard-error,northward-sea-water-velocity-standard-error,numerical-model,oceanographic-geographical-features,satellite-observation,surface-geostophic-eastward-sea-water-velocity-standard-error,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eastward-sea-water-velocity-due-to-ekman-drift-standard-error,eastward-sea-water-velocity-standard-error,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-nrt-0.25deg-p1d-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,northward-sea-water-velocity-due-to-ekman-drift-standard-error,northward-sea-water-velocity-standard-error,numerical-model,oceanographic-geographical-features,satellite-observation,surface-geostophic-eastward-sea-water-velocity-standard-error,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eastward-sea-water-velocity-due-to-ekman-drift-standard-error,eastward-sea-water-velocity-standard-error,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-nrt-0.25deg-p1m-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,northward-sea-water-velocity-due-to-ekman-drift-standard-error,northward-sea-water-velocity-standard-error,numerical-model,oceanographic-geographical-features,satellite-observation,surface-geostophic-eastward-sea-water-velocity-standard-error,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eastward-sea-water-velocity-due-to-ekman-drift-standard-error,eastward-sea-water-velocity-standard-error,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-nrt-0.25deg-pt1h-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,northward-sea-water-velocity-due-to-ekman-drift-standard-error,northward-sea-water-velocity-standard-error,numerical-model,oceanographic-geographical-features,satellite-observation,surface-geostophic-eastward-sea-water-velocity-standard-error,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-asc_P1D_202411": {"abstract": "'''Short description:'''\n\nThe product MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014 is a reformatting and a simplified version of the CATDS L3 product called \u201c2Q\u201d or \u201cL2Q\u201d. it is an intermediate product, that provides, in daily files, SSS corrected from land-sea contamination and latitudinal bias, with/without rain freshening correction.\n\n'''DOI (product) :''' \nhttps://doi.org/10.1016/j.rse.2016.02.061", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-sss-l3-mynrt-015-014:cmems-obs-mob-glo-phy-sss-mynrt-smos-asc-p1d-202411,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-sss-l3-mynrt-015-014:cmems-obs-mob-glo-phy-sss-mynrt-smos-des-p1d-202411,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L4_MY_015_015:cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W_202406": {"abstract": "'''Short description:'''\n\nThe product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called \u201cSMOS-OI\u201d. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information.\n\nKolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/\n\n'''DOI (product) :''' \nhttps://doi.org/10.1175/JTECH-D-20-0093.1", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-sss-l4-my-015-015:cmems-obs-mob-glo-phy-sss-my-multi-oi-p1w-202406,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,sea-surface-temperature,sea-water-conservative-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-05-31", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SSS SMOS/SMAP L4 OI - LOPS-v2023"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-my-multi-p1d-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1M_202311": {"abstract": "'''Short description:'''\n\nThis product consits of daily global gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/8\u00b0 of resolution, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA\u2019s Soil Moisture Active Passive (SMAP) and ESA\u2019s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite SST information. The product was developed by the Consiglio Nazionale delle Ricerche (CNR) and includes 4 datasets:\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1D, which provides near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1M, which provides near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy-sss_my_multi_P1D, which provides multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy-sss_my_multi_P1M, which provides multi-year reprocessed (REP) monthly data \n\n'''Product citation''': \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00051", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-my-multi-p1m-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-nrt-multi-p1d-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-nrt-multi-p1m-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-nrt-monthly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-weekly_202012": {"abstract": "'''Short description:'''\nYou can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing. It consists of 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/4 degree regular grid and on 50 depth levels from the surface down to the bottom. The product includes 4 datasets: \n* dataset-armor-3d-nrt-weekly, which delivers near-real-time (NRT) weekly data\n* dataset-armor-3d-nrt-monthly, which delivers near-real-time (NRT) monthly data\n* dataset-armor-3d-rep-weekly, which delivers multi-year reprocessed (REP) weekly data \n* dataset-armor-3d-rep-monthly, which delivers multi-year reprocessed (REP) monthly data\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00052\n\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-nrt-weekly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-rep-monthly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-rep-weekly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_W_3D_REP_015_007:cmems_obs-mob_glo_phy-cur_my_0.25deg_P7D-i_202411": {"abstract": "'''Short description''':\n\nYou can find here the OMEGA3D observation-based quasi-geostrophic vertical and horizontal ocean currents developed by the Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4\u00b0 horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_REP_015_002 which corresponds to former version of MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012) and ERA-Interim surface fluxes. \n\n'''DOI (product) :''' \nhttps://commons.datacite.org/doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007\n\n\n'''Product citation''': \nPlease refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:multiobs-glo-phy-w-3d-rep-015-007:cmems-obs-mob-glo-phy-cur-my-0.25deg-p7d-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2018-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D)"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThe NWSHELF_ANALYSISFORECAST_BGC_004_002 is produced by a coupled physical-biogeochemical model, implemented over the North East Atlantic and Shelf Seas at 1/20 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated weekly, providing 10-day forecast of the main biogeochemical variables.\nProducts are provided as daily and monthly means.\n\n'''Product Citation''':\nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00056", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-optics-anfc-0.027deg-p1d-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-optics-anfc-0.027deg-p1m-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-anfc-0.027deg-3d-p1d-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-anfc-0.027deg-3d-p1m-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThe NWSHELF_ANALYSISFORECAST_PHY_004_013 is produced by a hydrodynamic model with tides, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated daily, providing 5-day forecast for temperature, salinity, currents, sea level and mixed layer depth.\nProducts are provided at quarter-hourly, hourly, daily de-tided, and monthly frequency.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00054", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-cur-anfc-detided-0.027deg-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-cur-anfc-detided-0.027deg-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-ssh-anfc-detided-0.027deg-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-ssh-anfc-detided-0.027deg-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-wcur-anfc-0.027deg-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-wcur-anfc-0.027deg-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-2d-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-3d-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-3d-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-analysisforecast-wav-004-014:cmems-mod-nws-wav-anfc-0.027deg-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.05deg_PT1H-i_202309": {"abstract": "'''Short description:'''\n\nThe NWSHELF_ANALYSISFORECAST_WAV_004_014 is produced by a wave model system based on MFWAV, implemented over the North East Atlantic and Shelf Seas at 1/20 degrees of horizontal resolution forced by ECMWF wind data. The system assimilates significant wave height altimeter data and spectral data, and it is forced by currents provided by the [ ref t the physical system] ocean circulation system.\nThe product is updated twice a day, providing 10-day forecast of wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state, and wind-state, and swell components. \nProducts are provided at hourly frequency \n\n'''Product Citation''':\nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00055", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-analysisforecast-wav-004-014:cmems-mod-nws-wav-anfc-0.05deg-pt1h-i-202309,forecast,level-4,marine-resources,marine-safety,near-real-time,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-chl-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-chl-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-chl-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-kd-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-kd-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-kd-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-no3-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-no3-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-no3-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-o2-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-o2-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-o2-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-diato-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-diato-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-dino-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m_202012": {"abstract": "'''Short Description:'''\n\nThe ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butenschön et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009]. Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data.\n\nThe description of the model and its configuration, including the products validation is provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-011.pdf CMEMS-NWS-QUID-004-011]. \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually, providing a six-month extension of the time series. See [http://resources.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-009_011.pdf CMEMS-NWS-PUM-004-009_011] for details.\n\n'''Associated products:'''\n\nThis model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009].\nAn analysis-forecast product is available from: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011 NWSHELF_MULTIYEAR_BGC_004_011].\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00058", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-dino-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-nano-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-nano-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-pico-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-pico-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-diato-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-dino-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-nano-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-pico-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-ph-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-ph-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-ph-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-phyc-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-phyc-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-phyc-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-po4-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-po4-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-po4-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pp-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pp-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pp-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-spco2-my-7km-2d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-spco2-my-7km-2d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-spco2-myint-7km-2d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-my-7km-2d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-my-7km-2d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-myint-7km-2d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-my-7km-2d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-my-7km-2d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-myint-7km-2d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1D-m_202012": {"abstract": "'''Short Description:'''\n\nThe ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution. \nThe ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026 GLOBAL_REANALYSIS_PHY_001_026] and at the Baltic boundary by the [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_PHY_003_011 BALTICSEA_REANALYSIS_PHY_003_011]. The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. \n\nFurther details of the model, including the product validation are provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-009.pdf CMEMS-NWS-QUID-004-009]. \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually provinding six-month extension of the time series.\n\nSee [http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-009_011.pdf CMEMS-NWS-PUM-004-009_011] for further details.\n\n'''Associated products:'''\n\nThis model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011]. An analysis-forecast product is available from [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001 NWSHELF_ANALYSISFORECAST_PHY_LR_004_011].\nThe product is updated biannually provinding six-month extension of the time series.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00059", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-s-my-7km-3d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-s-my-7km-3d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-s-myint-7km-3d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-my-7km-2d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-my-7km-2d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-myint-7km-2d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-sss-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-sst-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-t-my-7km-3d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-t-my-7km-3d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-t-myint-7km-3d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-my-7km-3d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-my-7km-3d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-myint-7km-3d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_REANALYSIS_WAV_004_015:MetO-NWS-WAV-RAN_202007": {"abstract": "'''Short description:'''\n\nThis product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013 NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013]. The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ECMWF ERA-5] Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-015.pdf CMEMS-NWS-PUM-004-015] for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-015.pdf CMEMS-NWS-QUID-004-015].\nThe product is updated biannually provinding six-month extension of the time series.\n\n'''Associated products:'''\n\n[https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014 NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014].\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00060", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-reanalysis-wav-004-015:meto-nws-wav-ran-202007,level-4,marine-resources,marine-safety,multi-year,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Wave Physics Reanalysis"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l3-my-009-123:cmems-obs-oc-arc-bgc-plankton-my-l3-multi-4km-p1d-202311,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"abstract": "'''Short description:'''\n\nFor the '''Arctic''' Ocean '''Satellite Observations''', Italian National Research Council (CNR \u2013 Rome, Italy) is providing '''Bio-Geo_Chemical (BGC)''' products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the '''\"multi\"''' products and S3A & S3B only for the '''\"OLCI\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Diffuse Attenuation ('''KD490''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''4 km''' (multi) or '''300 m''' (OLCI).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00292", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l3-my-009-123:cmems-obs-oc-arc-bgc-reflectance-my-l3-multi-4km-p1d-202311,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l3-my-009-123:cmems-obs-oc-arc-bgc-transp-my-l3-multi-4km-p1d-202311,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L4_MY_009_124:cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M_202311": {"abstract": "'''Short description:'''\n\nFor the '''Arctic''' Ocean '''Satellite Observations''', Italian National Research Council (CNR \u2013 Rome, Italy) is providing '''Bio-Geo_Chemical (BGC)''' products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the '''\"multi\"''' products , and S3A & S3B only for the '''\"OLCI\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Diffuse Attenuation ('''KD490''')\n\n\n* Temporal resolutions: '''monthly'''.\n* Spatial resolutions: '''4 km''' (multi) or '''300 meters''' (OLCI).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00293", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l4-my-009-124:cmems-obs-oc-arc-bgc-plankton-my-l4-multi-4km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton MY L4 daily climatology and monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-optics-my-l3-multi-1km-p1d-202311,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-plankton-my-l3-multi-1km-p1d-202411,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-plankton-my-l3-olci-1km-p1d-202411,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-plankton-my-l3-olci-300m-p1d-202303,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-reflectance-my-l3-multi-1km-p1d-202311,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D_202303": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''1 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00286", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-reflectance-my-l3-olci-300m-p1d-202303,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-transp-my-l3-multi-1km-p1d-202311,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''1 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00284", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-optics-nrt-l3-multi-1km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-plankton-nrt-l3-multi-1km-p1d-202411,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-plankton-nrt-l3-olci-1km-p1d-202411,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-plankton-nrt-l3-olci-300m-p1d-202303,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-reflectance-nrt-l3-multi-1km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-reflectance-nrt-l3-olci-300m-p1d-202303,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-transp-nrt-l3-multi-1km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-my-009-118:cmems-obs-oc-atl-bgc-plankton-my-l4-gapfree-multi-1km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-my-009-118:cmems-obs-oc-atl-bgc-plankton-my-l4-multi-1km-p1m-202411,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: '''1 km'''.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00289", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-my-009-118:cmems-obs-oc-atl-bgc-pp-my-l4-multi-1km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-nrt-009-116:cmems-obs-oc-atl-bgc-plankton-nrt-l4-gapfree-multi-1km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-nrt-009-116:cmems-obs-oc-atl-bgc-plankton-nrt-l4-multi-1km-p1m-202411,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: '''1 km'''.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00288", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-nrt-009-116:cmems-obs-oc-atl-bgc-pp-nrt-l4-multi-1km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\n*cmems_obs_oc_bal_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00079", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-hr-l3-nrt-009-202:cmems-obs-oc-bal-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n*cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00080", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-hr-l4-nrt-009-208:cmems-obs-oc-bal-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-optics-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-plankton-my-l3-multi-1km-p1d-202411,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L3_MY\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00296", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-plankton-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-reflectance-my-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-reflectance-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-transp-my-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-transp-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-optics-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-plankton-nrt-l3-olci-300m-p1d-202411,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': OLCI-S3A & S3B \n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 300 meters \n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L3_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00294", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-reflectance-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-transp-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-plankton-my-l4-multi-1km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly\n\n'''Spatial resolution''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L4_MY\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00308", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-plankton-my-l4-olci-300m-p1m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-pp-my-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-pp-my-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_NRT_009_132:cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M_202411": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n'''Upstreams''': OLCI-S3A & S3B \n\n'''Temporal resolution''': monthly \n\n'''Spatial resolution''': 300 meters \n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L4_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00295", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-nrt-009-132:cmems-obs-oc-bal-bgc-plankton-nrt-l4-olci-300m-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided within 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\n*cmems_obs_oc_blk_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00086", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-hr-l3-nrt-009-206:cmems-obs-oc-blk-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n*cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00087", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-hr-l4-nrt-009-212:cmems-obs-oc-blk-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-optics-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-plankton-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-plankton-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-reflectance-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_BLK_BGC_L3_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00303", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-reflectance-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-transp-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-transp-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BLK_BGC_L3_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00301", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-optics-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-plankton-nrt-l3-multi-1km-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-plankton-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-reflectance-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-reflectance-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-transp-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-transp-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-gapfree-multi-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-multi-1km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated '''gap-free''' Chl concentration (to provide a \"cloud free\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"gap-free\"''' and climatology data)\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_BLK_BGC_L4_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00304", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-multi-climatology-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-olci-300m-p1m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-pp-my-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-pp-my-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-plankton-nrt-l4-gapfree-multi-1km-p1d-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated '''gap-free''' Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"\"multi'''\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"\"gap-free\"\"''' data)\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BLK_BGC_L4_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00302", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-plankton-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-plankton-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-pp-nrt-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-pp-nrt-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-transp-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-transp-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-optics-my-l3-multi-4km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-plankton-my-l3-multi-4km-p1d-202411,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-plankton-my-l3-olci-300m-p1d-202211,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-plankton-my-l3-olci-4km-p1d-202411,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-reflectance-my-l3-multi-4km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D_202207": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00280", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-reflectance-my-l3-olci-4km-p1d-202207,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-transp-my-l3-multi-4km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-transp-my-l3-olci-4km-p1d-202207,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-107:c3s-obs-oc-glo-bgc-plankton-my-l3-multi-4km-p1d-202303,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202303": {"abstract": "'''Short description:'''\n\nFor the '''Global''' Ocean '''Satellite Observations''', Brockmann Consult (BC) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''\"\"multi\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily''', '''monthly'''.\n* Spatial resolutions: '''4 km''' (multi).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find these products in the catalogue, use the search keyword '''\"\"ESA-CCI\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00282", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-107:c3s-obs-oc-glo-bgc-reflectance-my-l3-multi-4km-p1d-202303,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-optics-nrt-l3-multi-4km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-plankton-nrt-l3-multi-4km-p1d-202411,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-plankton-nrt-l3-olci-300m-p1d-202207,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-plankton-nrt-l3-olci-4km-p1d-202411,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-reflectance-nrt-l3-multi-4km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-reflectance-nrt-l3-olci-300m-p1d-202211,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-reflectance-nrt-l3-olci-4km-p1d-202207,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00278", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-transp-nrt-l3-multi-4km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-transp-nrt-l3-olci-4km-p1d-202207,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-optics-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and S3A & S3B only for the '''\"\"olci\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"\"cloud free\"\" product.\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"\"GlobColour\"\"'''.\"\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00281", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-gapfree-multi-4km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-multi-4km-p1m-202411,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-multi-climatology-4km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-olci-300m-p1m-202211,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-olci-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-pp-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-reflectance-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-reflectance-my-l4-olci-300m-p1m-202211,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-reflectance-my-l4-olci-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-transp-my-l4-gapfree-multi-4km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-transp-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-transp-my-l4-olci-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_108:c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202207": {"abstract": "'''Short description:'''\n\nFor the '''Global''' Ocean '''Satellite Observations''', Brockmann Consult (BC) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''\"\"multi\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL''').\n\n* Temporal resolutions: '''monthly'''.\n* Spatial resolutions: '''4 km''' (multi).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find these products in the catalogue, use the search keyword '''\"\"ESA-CCI\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00283", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-108:c3s-obs-oc-glo-bgc-plankton-my-l4-multi-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour Plankton MY L4 monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00279", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-optics-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-gapfree-multi-4km-p1d-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-multi-4km-p1m-202411,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-olci-300m-p1m-202211,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-olci-4km-p1m-202207,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-pp-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-reflectance-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-reflectance-nrt-l4-olci-300m-p1m-202211,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-reflectance-nrt-l4-olci-4km-p1m-202207,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-transp-nrt-l4-gapfree-multi-4km-p1d-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-transp-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-transp-nrt-l4-olci-4km-p1m-202207,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\n*cmems_obs_oc_nws_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00107", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-ibi-bgc-hr-l3-nrt-009-204:cmems-obs-oc-ibi-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Iberic Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n*cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00108", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-ibi-bgc-hr-l4-nrt-009-210:cmems-obs-oc-ibi-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Iberic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\n*cmems_obs_oc_ibi_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00109", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-hr-l3-nrt-009-205:cmems-obs-oc-med-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1-) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n*cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1M-v01+D19\n*cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00110", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-hr-l4-nrt-009-211:cmems-obs-oc-med-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D_202311": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"multi'''\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n* '''''pp''''' with the Integrated Primary Production (PP)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_MED_BGC_L3_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00299", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-optics-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-plankton-my-l3-multi-1km-p1d-202411,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-plankton-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-reflectance-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-reflectance-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-transp-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-transp-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-optics-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-plankton-nrt-l3-multi-1km-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"\"multi'''\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_MED_BGC_L3_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00297", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-plankton-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-reflectance-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-reflectance-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-transp-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-transp-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-gapfree-multi-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-multi-1km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-multi-climatology-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated '''gap-free''' Chl concentration (to provide a \"cloud free\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* '''''pp''''' with the Integrated Primary Production (PP).\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"gap-free\"''' and climatology data)\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_MED_BGC_L4_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00300", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-olci-300m-p1m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-pp-my-l4-multi-4km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-pp-my-l4-multi-4km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-plankton-nrt-l4-gapfree-multi-1km-p1d-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-plankton-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-plankton-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-pp-nrt-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-pp-nrt-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-transp-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated '''gap-free''' Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"\"multi'''\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"\"gap-free\"\"''' data)\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_MED_BGC_L4_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00298", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-transp-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\n*cmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00118", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-nws-bgc-hr-l3-nrt-009-203:cmems-obs-oc-nws-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North West Shelf Region, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n*cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00119", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-nws-bgc-hr-l4-nrt-009-209:cmems-obs-oc-nws-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North West Shelf Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-ssmi-merged-p30d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-ssmi-merged-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-p2d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P2D_202311": {"abstract": "'''Short description:''' \n\nAntarctic sea ice displacement during winter from medium resolution sensors since 2002\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00120", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-physic-my-drift-amsr-p2d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-physic-my-drift-amsr-p3d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021:cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m_202211": {"abstract": "'''Short description:''' \nArctic Sea and Ice surface temperature\n\n'''Detailed description:''' \nArctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00315", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-phy-climate-l3-my-011-021:cmems-obs-si-arc-phy-my-l3s-dmioi-p1d-m-202211,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-06-30", "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016:cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m_202105": {"abstract": "'''Short description:''' \nArctic Sea and Ice surface temperature\n\n'''Detailed description:'''\nArctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00123", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-phy-climate-l4-my-011-016:cmems-obs-si-arc-phy-my-l4-dmioi-p1d-m-202105,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-06-30", "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-30days-drift-ascat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-30days-drift-quickscat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-ascat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-ascat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-quickscat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-quickscat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-6days-drift-ascat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "'''Short description:''' \n\nArctic sea ice drift dataset at 3, 6 and 30 day lag during winter. The Arctic low resolution sea ice drift products provided from IFREMER have a 62.5 km grid resolution. They are delivered as daily products at 3, 6 and 30 days for the cold season extended at fall and spring: from September until May, it is updated on a monthly basis. The data are Merged product from radiometer and scatterometer :\n* SSM/I 85 GHz V & H Merged product (1992-1999)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00126", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-6days-drift-quickscat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-drift-my-l3-ssmi-p30d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-drift-my-l3-ssmi-p3d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-ssmi-merged-p30d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-ssmi-merged-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-p6d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008:DMI-ARC-SEAICE_TEMP-L4-NRT-OBS": {"abstract": "'''Short description:'''\n\nArctic Sea and Ice surface temperature product based upon observations from the Metop_A AVHRR instrument. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00130", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-seaice-l4-nrt-observations-011-008:dmi-arc-seaice-temp-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea and Ice Surface Temperature"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m_202211": {"abstract": "Gridded sea ice concentration, sea ice extent and classification based on the digitized Baltic ice charts produced by the FMI/SMHI ice analysts. It is produced daily in the afternoon, describing the ice situation daily at 14:00 EET. The nominal resolution is about 1km. The temporal coverage is from the beginning of the season 1980-1981 until today.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00131", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-phy-l4-my-011-019:cmems-obs-si-bal-phy-sit-my-l4-1km-p1d-m-202211,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-classification,sea-ice-concentration,sea-ice-extent,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2024-06-04", "missionStartDate": "1981-12-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea ice concentration, extent, and classification time series"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112": {"abstract": "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-phy-l4-my-011-019:cmems-obs-si-bal-seaice-conc-my-1km-202112,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-classification,sea-ice-concentration,sea-ice-extent,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2024-06-04", "missionStartDate": "1981-12-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea ice concentration, extent, and classification time series"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_CONC-L4-NRT-OBS": {"abstract": "'''Short description:''' \n\nFor the Baltic Sea- The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The parameters are based on ice chart produced on daily basis during the Baltic Sea ice season and show the ice concentration in a 1 km grid. Ice thickness chart (ITC) is a product based on the most recent available ice chart (IC) and a SAR image. The SAR data is used to update the ice information in the IC. The ice regions in the IC are updated according to a SAR segmentation and new ice thickness values are assigned to each SAR segment based on the SAR backscattering and the ice IC thickness range at that location.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00132", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-seaice-l4-nrt-observations-011-004:fmi-bal-seaice-conc-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-extent,sea-ice-thickness,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS": {"abstract": "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-seaice-l4-nrt-observations-011-004:fmi-bal-seaice-thick-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-extent,sea-ice-thickness,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "EO:MO:DAT:SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013:c3s_obs-si_glo_phy_my_nh-l3_P1M_202411": {"abstract": "'''Short description:'''\n\nArctic sea ice L3 data in separate monthly files. The time series is based on reprocessed radar altimeter satellite data from Envisat and CryoSat and is available in the freezing season between October and April. The product is brokered from the Copernicus Climate Change Service (C3S).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00127", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-phy-climate-l3-my-011-013:c3s-obs-si-glo-phy-my-nh-l3-p1m-202411,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2015-04-01", "missionStartDate": "2002-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea Ice Thickness REPROCESSED"}, "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411": {"abstract": "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411", "instrument": null, "keywords": "eo:mo:dat:seaice-glo-phy-l4-nrt-011-014:esa-obs-si-arc-phy-sit-nrt-l4-multi-p1d-m-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_nh_P1D-m_202411": {"abstract": "'''Short description:''' \n\nFor the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers five global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401, OSI-402, OSI-403, OSI-405 and OSI-408). The sea ice concentration, edge and type products are delivered daily at 10km resolution and the sea ice drift in 62.5km resolution, all in polar stereographic projections covering the Northern Hemisphere and the Southern Hemisphere. The sea ice drift motion vectors have a time-span of 2 days. These are the Sea Ice operational nominal products for the Global Ocean.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00134", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-nrt-observations-011-001:osisaf-obs-si-glo-phy-sidrift-nrt-nh-p1d-m-202411,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,sea-ice-x-displacement,sea-ice-y-displacement,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411": {"abstract": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-nrt-observations-011-001:osisaf-obs-si-glo-phy-sidrift-nrt-sh-p1d-m-202411,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,sea-ice-x-displacement,sea-ice-y-displacement,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS_202003": {"abstract": "'''Short description:''' \nThe CDR and ICDR sea ice concentration dataset of the EUMETSAT OSI SAF (OSI-450-a and OSI-430-a), covering the period from October 1978 to present, with 16 days delay. It used passive microwave data from SMMR, SSM/I and SSMIS. Sea ice concentration is computed from atmospherically corrected PMW brightness temperatures, using a combination of state-of-the-art algorithms and dynamic tie points. It includes error bars for each grid cell (uncertainties). This version 3.0 of the CDR (OSI-450-a, 1978-2020) and ICDR (OSI-430-a, 2021-present with 16 days latency) was released in November 2022\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00136", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-rep-observations-011-009:osisaf-glo-seaice-conc-cont-timeseries-nh-la-obs-202003,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1979-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003": {"abstract": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-rep-observations-011-009:osisaf-glo-seaice-conc-cont-timeseries-sh-la-obs-202003,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1979-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_al-l3-duacs_PT1S_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n\u201c\u2019Associated products\u201d\u2019\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00139", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-al-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_alg-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_alg-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-alg-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-c2-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-c2n-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-e1-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1g-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1g-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-e1g-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-e2-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_en-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_en-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-en-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_enn-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_enn-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-enn-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_g2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_g2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-g2-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2a-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2a-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-h2a-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2ag-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2ag-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-h2ag-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2b-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2b-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-h2b-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j1-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1g-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1g-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j1g-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j1n-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j2-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2g-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2g-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j2g-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j2n-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j3-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j3n-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3a-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3a-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-s3a-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3b-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3b-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-s3b-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-s6a-lr-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swon-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swon-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-swon-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swonc-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swonc-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-swonc-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tp-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tp-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-tp-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tpn-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tpn-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-tpn-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1D_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00141", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-my-008-068:cmems-obs-sl-eur-phy-ssh-my-allsat-l4-duacs-0.0625deg-p1d-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-my-008-068:cmems-obs-sl-eur-phy-ssh-my-allsat-l4-duacs-0.0625deg-p1m-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202311": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00142", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-nrt-008-060:cmems-obs-sl-eur-phy-ssh-nrt-allsat-l4-duacs-0.125deg-p1d-202311,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D_202411": {"abstract": "'''Short description:''' \n\nDUACS delayed-time altimeter gridded maps of sea surface heights and derived variables over the global Ocean (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global?tab=overview). The processing focuses on the stability and homogeneity of the sea level record (based on a stable two-satellite constellation) and the product is dedicated to the monitoring of the sea level long-term evolution for climate applications and the analysis of Ocean/Climate indicators. These products are produced and distributed by the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/).\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00145", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-climate-l4-my-008-057:c3s-obs-sl-glo-phy-ssh-my-twosat-l4-duacs-0.25deg-p1d-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-climate-l4-my-008-057:c3s-obs-sl-glo-phy-ssh-my-twosat-l4-duacs-0.25deg-p1m-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_al-l3-duacs_PT1S_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc.). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n'''Associated products'''\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n'''DOI (product)''':\nhttps://doi.org/10.48670/moi-00146", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-al-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_alg-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_alg-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-alg-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-c2-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-c2n-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-e1-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1g-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1g-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-e1g-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-e2-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_en-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_en-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-en-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_enn-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_enn-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-enn-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_g2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_g2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-g2-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2a-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2a-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-h2a-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2ag-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2ag-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-h2ag-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2b-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2b-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-h2b-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j1-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j1n-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j2-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2g-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2g-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j2g-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j2n-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j3-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j3n-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3a-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3a-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-s3a-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3b-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3b-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-s3b-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-s6a-lr-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swon-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swon-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-swon-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swonc-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swonc-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-swonc-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tp-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tp-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-tp-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tpn-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tpn-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-tpn-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00148", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-my-008-047:cmems-obs-sl-glo-phy-ssh-my-allsat-l4-duacs-0.125deg-p1d-202411,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-my-008-047:cmems-obs-sl-glo-phy-ssh-my-allsat-l4-duacs-0.125deg-p1m-m-202411,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D_202311": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00149", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-nrt-008-046:cmems-obs-sl-glo-phy-ssh-nrt-allsat-l4-duacs-0.25deg-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SST_ATL_PHY_L3S_MY_010_038:cmems_obs-sst_atl_phy_my_l3s_P1D-m_202411": {"abstract": "'''Short description:'''\n\nFor the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-my-010-038:cmems-obs-sst-atl-phy-my-l3s-p1d-m-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Reprocessed"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_gir_P1D-m_202311": {"abstract": "'''Short description:'''\n\nFor the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02\u00b0 resolution grid. It includes observations by polar orbiting and geostationary satellites . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. 3 more datasets are available that only contain \"per sensor type\" data : Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00310", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-l3s-gir-p1d-m-202311,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-l3s-pir-p1d-m-202311,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-l3s-pmw-p1d-m-202311,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211": {"abstract": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-nrt-l3s-p1d-m-202211,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025:IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE_201904": {"abstract": "'''Short description:'''\n\nFor the Atlantic European North West Shelf Ocean-European North West Shelf/Iberia Biscay Irish Seas. The ODYSSEA NW+IBI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg x 0.02deg horizontal resolution, using satellite data from both infra-red and micro-wave radiometers. It is the sea surface temperature operational nominal product for the Northwest Shelf Sea and Iberia Biscay Irish Seas.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00152", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-sst-l4-nrt-observations-010-025:ifremer-atl-sst-l4-nrt-obs-full-time-serie-201904,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2018-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA L4 Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_ATL_SST_L4_REP_OBSERVATIONS_010_026:cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE_202411": {"abstract": "'''Short description:''' \n\nFor the European North West Shelf Ocean Iberia Biscay Irish Seas. The IFREMER Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.05deg. x 0.05deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00153", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-sst-l4-rep-observations-010-026:cmems-ifremer-atl-sst-l4-rep-obs-full-time-serie-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas - High Resolution L4 Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BAL_PHY_L3S_MY_010_040:cmems_obs-sst_bal_phy_my_l3s_P1D-m_202211": {"abstract": "'''Short description:''' \nFor the Baltic Sea- the DMI Sea Surface Temperature reprocessed L3S aims at providing daily multi-sensor supercollated data at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00312", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-phy-l3s-my-010-040:cmems-obs-sst-bal-phy-my-l3s-p1d-m-202211,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - L3S Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BAL_PHY_SUBSKIN_L4_NRT_010_034:cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m_202211": {"abstract": "'''Short description:'''\nFor the Baltic Sea - the DMI Sea Surface Temperature Diurnal Subskin L4 aims at providing hourly analysis of the diurnal subskin signal at 0.02deg. x 0.02deg. horizontal resolution, using the BAL L4 NRT product as foundation temperature and satellite data from infra-red radiometers. Uses SST satellite products from the sensors: Metop B AVHRR, Sentinel-3 A/B SLSTR, VIIRS SUOMI NPP & NOAA20 \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00309", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-phy-subskin-l4-nrt-010-034:cmems-obs-sst-bal-phy-subskin-nrt-l4-pt1h-m-202211,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-05-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032:DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE_201904": {"abstract": "'''Short description:''' \n\nFor the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00154", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-sst-l3s-nrt-observations-010-032:dmi-baltic-sst-l3s-nrt-obs-full-time-serie-201904,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Sea/Baltic Sea - Sea Surface Temperature Analysis L3S"}, "EO:MO:DAT:SST_BAL_SST_L4_REP_OBSERVATIONS_010_016:DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016_202012": {"abstract": "'''Short description:''' \nFor the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. The product uses SST satellite products from the ESA CCI and Copernicus C3S projects, including the sensors: NOAA AVHRRs 7, 9, 11, 12, 14, 15, 16, 17, 18 , 19, Metop, ATSR1, ATSR2, AATSR and SLSTR.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00156", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-sst-l4-rep-observations-010-016:dmi-bal-sst-l4-rep-observations-010-016-202012,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea- Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BS_PHY_L3S_MY_010_041:cmems_obs-sst_bs_phy_my_l3s_P1D-m_202411": {"abstract": "'''Short description:''' \n\nThe Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from January 1st 1982 to present (currently, up to one month before real time). The BS-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives, but also includes in input an adjusted version of the AVHRR Pathfinder dataset version 5.3 to increase the input observation coverage. Due to Brexit, an interim production guarantees the temporal extension of the BS-REP-L3S product since 1st January 2023 to present.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00313", "instrument": null, "keywords": "adjusted-sea-surface-temperature,black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-phy-l3s-my-010-041:cmems-obs-sst-bs-phy-my-l3s-p1d-m-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1981-08-25", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BS_PHY_SUBSKIN_L4_NRT_010_035:cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"abstract": "'''Short description:'''\n\nFor the Black Sea - the CNR diurnal sub-skin Sea Surface Temperature product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Black Sea (BS) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS BS Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00157", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-phy-subskin-l4-nrt-010-035:cmems-obs-sst-blk-phy-sst-nrt-diurnal-oi-0.0625deg-pt1h-m-202105,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a_202311": {"abstract": "'''Short description:''' \n\nFor the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Black Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00158", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l3s-nrt-observations-010-013:sst-bs-sst-l3s-nrt-observations-010-013-a-202311,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311": {"abstract": "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l3s-nrt-observations-010-013:sst-bs-sst-l3s-nrt-observations-010-013-b-202311,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b": {"abstract": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-ssta-l4-nrt-observations-010-006-b,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d": {"abstract": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-ssta-l4-nrt-observations-010-006-d,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2_202311": {"abstract": "'''Short description:''' \n\nFor the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain providess daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Black Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00159", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-sst-l4-nrt-observations-010-006-a-v2-202311,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311": {"abstract": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-sst-l4-nrt-observations-010-006-c-v2-202311,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_REP_OBSERVATIONS_010_022:cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022_202411": {"abstract": "'''Short description:''' \n\nThe Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from January 1st 1982 to present (currently, up to one month before real time). The BS-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives, but also includes in input an adjusted version of the AVHRR Pathfinder dataset version 5.3 to increase the input observation coverage. Due to Brexit, an interim production guarantees the temporal extension of the BS-REP-L4 product since 1st January 2023 to present.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00160", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-rep-observations-010-022:cmems-sst-bs-sst-l4-rep-observations-010-022-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1981-08-25", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_GLO_PHY_L3S_MY_010_039:cmems_obs-sst_glo_phy_my_l3s_P1D-m_202311": {"abstract": "'''Short description:''' \n\nFor the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. \n\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00329", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-phy-l3s-my-010-039:cmems-obs-sst-glo-phy-my-l3s-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_PHY_L4_NRT_010_043:cmems_obs-sst_glo_phy_nrt_l4_P1D-m_202303": {"abstract": "This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours.\n\nWhereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed.\n\nThe processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00321", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-phy-l4-nrt-010-043:cmems-obs-sst-glo-phy-nrt-l4-p1d-m-202303,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Sea Surface Temperature Gridded Level 4 Daily Multi-Sensor Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE_202211": {"abstract": "'''Short description:'''\n\nFor the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.1\u00b0 resolution global grid. It includes observations by polar orbiting (NOAA-18 & NOAAA-19/AVHRR, METOP-A/AVHRR, ENVISAT/AATSR, AQUA/AMSRE, TRMM/TMI) and geostationary (MSG/SEVIRI, GOES-11) satellites . The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.3 more datasets are available that only contain \"per sensor type\" data : Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00164", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:ifremer-glob-sst-l3-nrt-obs-full-time-serie-202211,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:cmems-obs-sst-glo-phy-l3s-gir-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:cmems-obs-sst-glo-phy-l3s-pir-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:cmems-obs-sst-glo-phy-l3s-pmw-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2": {"abstract": "'''Short description:''' \n\nFor the Global Ocean- the OSTIA global foundation Sea Surface Temperature product provides daily gap-free maps of : Foundation Sea Surface Temperature at 0.05\u00b0 x 0.05\u00b0 horizontal grid resolution, using in-situ and satellite data from both infrared and microwave radiometers. \n\nThe Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the UK's Met Office and delivered by IFREMER PU. OSTIA uses satellite data provided by the GHRSST project together with in-situ observations to determine the sea surface temperature.\nA high resolution (1/20\u00b0 - approx. 6 km) daily analysis of sea surface temperature (SST) is produced for the global ocean and some lakes.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00165", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l4-nrt-observations-010-001:metoffice-glo-sst-l4-nrt-obs-sst-v2,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:METOFFICE-GLO-SST-L4-REP-OBS-SST_202003": {"abstract": "'''Short description :'''\n\nThe OSTIA (Good et al., 2020) global sea surface temperature reprocessed product provides daily gap-free maps of foundation sea surface temperature and ice concentration (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal grid resolution, using in-situ and satellite data. This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00168", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,canary-current-system,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:sst-glo-sst-l4-rep-observations-010-011:metoffice-glo-sst-l4-rep-obs-sst-202003,global-ocean,level-4,marine-resources,marine-safety,modelling-data,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,south-brazilian-shelf,south-mid-atlantic-ridge,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-05-31", "missionStartDate": "1981-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:C3S-GLO-SST-L4-REP-OBS-SST_202211": {"abstract": "'''Short description:''' \nThe ESA SST CCI and C3S global Sea Surface Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the (A)ATSRs, SLSTR and the AVHRR series of sensors (Merchant et al., 2019). The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00169", "instrument": null, "keywords": "analysed-sst-uncertainty,coastal-marine-environment,eo:mo:dat:sst-glo-sst-l4-rep-observations-010-024:c3s-glo-sst-l4-rep-obs-sst-202211,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-water-temperature,sea-water-temperature-standard-error,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-31", "missionStartDate": "1981-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211": {"abstract": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211", "instrument": null, "keywords": "analysed-sst-uncertainty,coastal-marine-environment,eo:mo:dat:sst-glo-sst-l4-rep-observations-010-024:esacci-glo-sst-l4-rep-obs-sst-202211,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-water-temperature,sea-water-temperature-standard-error,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-31", "missionStartDate": "1981-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "EO:MO:DAT:SST_MED_PHY_L3S_MY_010_042:cmems_obs-sst_med_phy_my_l3s_P1D-m_202411": {"abstract": "'''Short description:''' \n\nThe Reprocessed (REP) Mediterranean Sea (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from January 1st 1982 to present (currently, up to one month before real time). The MED-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives, but also includes in input an adjusted version of the AVHRR Pathfinder dataset version 5.3 to increase the input observation coverage. Due to Brexit, an interim production guarantees the temporal extension of the MED-REP-L3S product since 1st January 2023 to present.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00314", "instrument": null, "keywords": "adjusted-sea-surface-temperature,coastal-marine-environment,eo:mo:dat:sst-med-phy-l3s-my-010-042:cmems-obs-sst-med-phy-my-l3s-p1d-m-202411,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1981-08-25", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_MED_PHY_SUBSKIN_L4_NRT_010_036:cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"abstract": "''' Short description: ''' \n\nFor the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n \n[https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-reference-copernicus-marine-products-and-services How to cite]\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00170", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-phy-subskin-l4-nrt-010-036:cmems-obs-sst-med-phy-sst-nrt-diurnal-oi-0.0625deg-pt1h-m-202105,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311": {"abstract": "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l3s-nrt-observations-010-012:sst-med-sst-l3s-nrt-observations-010-012-a-202311,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b_202311": {"abstract": "'''Short description:''' \n\nFor the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Mediterranean Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00171", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l3s-nrt-observations-010-012:sst-med-sst-l3s-nrt-observations-010-012-b-202311,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b": {"abstract": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-ssta-l4-nrt-observations-010-004-b,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d": {"abstract": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-ssta-l4-nrt-observations-010-004-d,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311": {"abstract": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-sst-l4-nrt-observations-010-004-a-v2-202311,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2_202311": {"abstract": "'''Short description:''' \n\nFor the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Mediterranean Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00172", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-sst-l4-nrt-observations-010-004-c-v2-202311,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_REP_OBSERVATIONS_010_021:cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021_202411": {"abstract": "'''Short description:''' \n \nThe Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from January 1st 1982 to present (currently, up to one month before real time). The MED-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives, but also includes in input an adjusted version of the AVHRR Pathfinder dataset version 5.3 to increase the input observation coverage. Due to Brexit, an interim production guarantees the temporal extension of the MED-REP-L4 product since 1st January 2023 to present.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00173", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-rep-observations-010-021:cmems-sst-med-sst-l4-rep-observations-010-021-202411,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:WAVE_GLO_PHY_SPC_L4_NRT_014_004:cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H_202112": {"abstract": "'''Short description:'''\n\nNear-Real-Time multi-mission global satellite-based spectral integral parameters. Only valid data are used, based on the L3 corresponding product. Included wave parameters are partition significant wave height, partition peak period and partition peak or principal direction. Those parameters are propagated in space and time at a 3-hour timestep and on a regular space grid, providing information of the swell propagation characteristics, from source to land. One file gathers one swell system, gathering observations originating from the same storm source. This product is processed by the WAVE-TAC multi-mission SAR data processing system to serve in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B. All the spectral parameter measurements are optimally interpolated using swell observations belonging to the same swell field. The SAR data processing system produces wave integral parameters by partition (partition significant wave height, partition peak period and partition peak or principal direction) and the associated standard deviation and density of propagated observations. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00175", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-spc-l4-nrt-014-004:cmems-obs-wave-glo-phy-spc-nrt-multi-l4-1deg-pt3h-202112,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-0.5deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nMulti-Year gridded multi-mission merged satellite significant wave height. Only valid data are included. This Multi-Year product is processed by the WAVE-TAC multi-mission altimeter data processing system and is based on CMEMS Multi-Year level-3 SWH datasets (see the product WAVE_GLO_PHY_SWH_L3_MY_014_005).\nIt merges along-track SWH data from the following missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa, Jason-3 and CFOSAT. The resulting gridded product has a 2\u00b0 horizontal resolution and is produced daily. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00177", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-my-014-007:cmems-obs-wave-glo-phy-swh-my-multi-l4-0.5deg-p1d-i-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,sea-surface-wave-significant-height-daily-maximum,sea-surface-wave-significant-height-daily-mean,sea-surface-wave-significant-height-daily-number-of-observations,sea-surface-wave-significant-height-daily-standard-deviation,sea-surface-wave-significant-height-mapping-score,sea-surface-wave-significant-height-number-of-observations,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2002-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411": {"abstract": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-my-014-007:cmems-obs-wave-glo-phy-swh-my-multi-l4-2deg-p1d-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,sea-surface-wave-significant-height-daily-maximum,sea-surface-wave-significant-height-daily-mean,sea-surface-wave-significant-height-daily-number-of-observations,sea-surface-wave-significant-height-daily-standard-deviation,sea-surface-wave-significant-height-mapping-score,sea-surface-wave-significant-height-number-of-observations,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2002-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411": {"abstract": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-nrt-014-003:cmems-obs-wave-glo-phy-swh-nrt-multi-l4-2deg-p1d-i-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-06-26", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411": {"abstract": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-nrt-014-003:cmems-obs-wave-glo-phy-swh-nrt-multi-l4-2deg-p1d-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-06-26", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D_202211": {"abstract": "'''Short description:'''\n\nNear-Real-Time gridded multi-mission merged satellite significant wave height. Only valid data are included. This product is processed in Near-Real-Time by the WAVE-TAC multi-mission altimeter data processing system and is based on CMEMS level-3 SWH datasets (see the product WAVE_GLO_WAV_L3_SWH_NRT_OBSERVATIONS_014_001).\nIt merges along-track SWH data from the following missions: Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT and HaiYang-2B. The resulting gridded product has a 2\u00b0 horizontal resolution and is produced daily. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00180", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-nrt-014-003:cmems-obs-wave-glo-phy-swh-nrt-multi-l4-2deg-p1d-202211,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-06-26", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WIND_GLO_PHY_CLIMATE_L4_MY_012_003:cmems_obs-wind_glo_phy_my_l4_P1M_202411": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT, QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT satellite instruments. The product provides monthly mean stress-equivalent wind and stress variables as well as their standard deviation. The number of observations used to calculate the monthly averages are included in the product.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00181", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-climate-l4-my-012-003:cmems-obs-wind-glo-phy-my-l4-p1m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-05-16", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers1-scat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers1-scat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers2-scat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers2-scat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-asc-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-des-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-asc-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-des-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"abstract": "'''Short description:''' \n\nFor the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n*0.5 degrees grid for the 50 km scatterometer L2 inputs,\n*0.25 degrees grid based on 25 km scatterometer swath observations,\n*and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. \n\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The MY L3 products follow the availability of the reprocessed EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees;\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00183", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-asc-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-des-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-asc-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-des-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i_202311": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n\n*0.5 degrees grid for the 50 km scatterometer L2 inputs,\n*0.25 degrees grid based on 25 km scatterometer swath observations,\n*and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products.\n\nData from ascending and descending passes are gridded separately.\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The NRT L3 products follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for:\n*The ASCAT scatterometers on Metop-A (discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees;\n*The OSCAT scatterometer on Scatsat-1 (discontinued on 28/02/2021) and Oceansat-3 at 0.25 and 0.5 degrees; \n*The HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees\n\nIn addition, the product includes European Centre for Medium-Range Weather Forecasts (ECMWF) operational model forecast wind and stress variables collocated with the scatterometer observations at L2 and processed to L3 in exactly the same way as the scatterometer observations.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00182", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-asc-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-des-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-asc-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-des-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-asc-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-des-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-asc-0.25deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-asc-0.5deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-des-0.25deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-des-0.5deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l4-my-012-006:cmems-obs-wind-glo-phy-my-l4-0.125deg-pt1h-202211,global-ocean,level-4,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-06-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H_202406": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 and 0.25 degrees horizontal spatial resolution. Scatterometer observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF ERA5 model fields. Bias corrections are based on scatterometer observations from Metop-A, Metop-B, Metop-C ASCAT (0.125 degrees) and QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT (0.25 degrees). The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00185", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l4-my-012-006:cmems-obs-wind-glo-phy-my-l4-0.25deg-pt1h-202406,global-ocean,level-4,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-06-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_GLO_PHY_L4_NRT_012_004:cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H_202207": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00305", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l4-nrt-012-004:cmems-obs-wind-glo-phy-nrt-l4-0.125deg-pt1h-202207,global-ocean,level-4,marine-resources,marine-safety,near-real-time,northward-wind,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model"}}, "providers_config": {"EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1D-m_202105": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1D-m_202105"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1D-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1D-m_202311"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011:cmems_mod_arc_phy_anfc_nextsim_P1M-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011:cmems_mod_arc_phy_anfc_nextsim_P1M-m_202311"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1D-m_202105": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1D-m_202105"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1M_202012": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1M_202012"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_7-10days_P1D-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_7-10days_P1D-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1M-m_202311": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1M-m_202311"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_PT1H-i_202311": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_PT1H-i_202311"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1Y-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1Y-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1D-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1D-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_PT1H-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_PT1H-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_PT1H-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_PT1H-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_WAV_007_003:cmems_mod_blk_wav_anfc_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_WAV_007_003:cmems_mod_blk_wav_anfc_2.5km_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_WAV_001_027:cmems_mod_glo_wav_anfc_0.083deg_PT3H-i_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_WAV_001_027:cmems_mod_glo_wav_anfc_0.083deg_PT3H-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i_202411": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_myint_0.2deg_PT3H-i_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_myint_0.2deg_PT3H-i_202311"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.05deg_PT1H-i_202311": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.05deg_PT1H-i_202311"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m_202211": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m_202211"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m_202211": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m_202211"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m_202311"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411"}, "EO:MO:DAT:INSITU_GLO_BGC_DISCRETE_MY_013_046:cmems_obs-ins_glo_bgc-nut_my_na_irr_202411": {"collection": "EO:MO:DAT:INSITU_GLO_BGC_DISCRETE_MY_013_046:cmems_obs-ins_glo_bgc-nut_my_na_irr_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_PT1H_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_PT1H_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_irr_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_irr_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_ibi_phy-ssh_my_tide-surge_PT1H_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_ibi_phy-ssh_my_tide-surge_PT1H_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_MY_013_052:cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_MY_013_052:cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_NRT_013_002:cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_NRT_013_002:cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_adcp_irr_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_adcp_irr_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_argo_irr_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_argo_irr_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_drifter_PT1H_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_drifter_PT1H_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_glider_irr_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_glider_irr_202411"}, "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_PT1H_202411": {"collection": "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_PT1H_202411"}, "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_irr_202411": {"collection": "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_irr_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202311": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202311"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202311": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202311"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_WAV_006_017:cmems_mod_med_wav_anfc_4.2km_PT1H-i_202311": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_WAV_006_017:cmems_mod_med_wav_anfc_4.2km_PT1H-i_202311"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-h_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-h_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_my_4.2km-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_my_4.2km-climatology_P1M-m_202311"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg-climatology_P1M-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-asc_P1D_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-asc_P1D_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L4_MY_015_015:cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W_202406": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L4_MY_015_015:cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W_202406"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1M_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1M_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-weekly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-weekly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_W_3D_REP_015_007:cmems_obs-mob_glo_phy-cur_my_0.25deg_P7D-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_W_3D_REP_015_007:cmems_obs-mob_glo_phy-cur_my_0.25deg_P7D-i_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.05deg_PT1H-i_202309": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.05deg_PT1H-i_202309"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_REANALYSIS_WAV_004_015:MetO-NWS-WAV-RAN_202007": {"collection": "EO:MO:DAT:NWSHELF_REANALYSIS_WAV_004_015:MetO-NWS-WAV-RAN_202007"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L4_MY_009_124:cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L4_MY_009_124:cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_NRT_009_132:cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_NRT_009_132:cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_108:c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_108:c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P2D_202311": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P2D_202311"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021:cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m_202211": {"collection": "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021:cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m_202211"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016:cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m_202105": {"collection": "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016:cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m_202105"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008:DMI-ARC-SEAICE_TEMP-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008:DMI-ARC-SEAICE_TEMP-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m_202211": {"collection": "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m_202211"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112": {"collection": "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_CONC-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_CONC-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013:c3s_obs-si_glo_phy_my_nh-l3_P1M_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013:c3s_obs-si_glo_phy_my_nh-l3_P1M_202411"}, "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_nh_P1D-m_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_nh_P1D-m_202411"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS_202003": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS_202003"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_al-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_al-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_alg-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_alg-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1g-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1g-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_en-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_en-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_enn-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_enn-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_g2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_g2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2a-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2a-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2ag-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2ag-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2b-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2b-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1g-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1g-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2g-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2g-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3a-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3a-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3b-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3b-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swon-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swon-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swonc-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swonc-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tp-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tp-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tpn-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tpn-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202311": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202311"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_al-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_al-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_alg-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_alg-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1g-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1g-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_en-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_en-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_enn-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_enn-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_g2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_g2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2a-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2a-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2ag-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2ag-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2b-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2b-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2g-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2g-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3a-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3a-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3b-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3b-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swon-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swon-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swonc-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swonc-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tp-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tp-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tpn-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tpn-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D_202311": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_MY_010_038:cmems_obs-sst_atl_phy_my_l3s_P1D-m_202411": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_MY_010_038:cmems_obs-sst_atl_phy_my_l3s_P1D-m_202411"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_gir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_gir_P1D-m_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211"}, "EO:MO:DAT:SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025:IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE_201904": {"collection": "EO:MO:DAT:SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025:IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE_201904"}, "EO:MO:DAT:SST_ATL_SST_L4_REP_OBSERVATIONS_010_026:cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE_202411": {"collection": "EO:MO:DAT:SST_ATL_SST_L4_REP_OBSERVATIONS_010_026:cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE_202411"}, "EO:MO:DAT:SST_BAL_PHY_L3S_MY_010_040:cmems_obs-sst_bal_phy_my_l3s_P1D-m_202211": {"collection": "EO:MO:DAT:SST_BAL_PHY_L3S_MY_010_040:cmems_obs-sst_bal_phy_my_l3s_P1D-m_202211"}, "EO:MO:DAT:SST_BAL_PHY_SUBSKIN_L4_NRT_010_034:cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m_202211": {"collection": "EO:MO:DAT:SST_BAL_PHY_SUBSKIN_L4_NRT_010_034:cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m_202211"}, "EO:MO:DAT:SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032:DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE_201904": {"collection": "EO:MO:DAT:SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032:DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE_201904"}, "EO:MO:DAT:SST_BAL_SST_L4_REP_OBSERVATIONS_010_016:DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016_202012": {"collection": "EO:MO:DAT:SST_BAL_SST_L4_REP_OBSERVATIONS_010_016:DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016_202012"}, "EO:MO:DAT:SST_BS_PHY_L3S_MY_010_041:cmems_obs-sst_bs_phy_my_l3s_P1D-m_202411": {"collection": "EO:MO:DAT:SST_BS_PHY_L3S_MY_010_041:cmems_obs-sst_bs_phy_my_l3s_P1D-m_202411"}, "EO:MO:DAT:SST_BS_PHY_SUBSKIN_L4_NRT_010_035:cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"collection": "EO:MO:DAT:SST_BS_PHY_SUBSKIN_L4_NRT_010_035:cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a_202311"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2_202311"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311"}, "EO:MO:DAT:SST_BS_SST_L4_REP_OBSERVATIONS_010_022:cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022_202411": {"collection": "EO:MO:DAT:SST_BS_SST_L4_REP_OBSERVATIONS_010_022:cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022_202411"}, "EO:MO:DAT:SST_GLO_PHY_L3S_MY_010_039:cmems_obs-sst_glo_phy_my_l3s_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_PHY_L3S_MY_010_039:cmems_obs-sst_glo_phy_my_l3s_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_PHY_L4_NRT_010_043:cmems_obs-sst_glo_phy_nrt_l4_P1D-m_202303": {"collection": "EO:MO:DAT:SST_GLO_PHY_L4_NRT_010_043:cmems_obs-sst_glo_phy_nrt_l4_P1D-m_202303"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE_202211": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE_202211"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:METOFFICE-GLO-SST-L4-REP-OBS-SST_202003": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:METOFFICE-GLO-SST-L4-REP-OBS-SST_202003"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:C3S-GLO-SST-L4-REP-OBS-SST_202211": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:C3S-GLO-SST-L4-REP-OBS-SST_202211"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211"}, "EO:MO:DAT:SST_MED_PHY_L3S_MY_010_042:cmems_obs-sst_med_phy_my_l3s_P1D-m_202411": {"collection": "EO:MO:DAT:SST_MED_PHY_L3S_MY_010_042:cmems_obs-sst_med_phy_my_l3s_P1D-m_202411"}, "EO:MO:DAT:SST_MED_PHY_SUBSKIN_L4_NRT_010_036:cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"collection": "EO:MO:DAT:SST_MED_PHY_SUBSKIN_L4_NRT_010_036:cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b_202311"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2_202311"}, "EO:MO:DAT:SST_MED_SST_L4_REP_OBSERVATIONS_010_021:cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021_202411": {"collection": "EO:MO:DAT:SST_MED_SST_L4_REP_OBSERVATIONS_010_021:cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SPC_L4_NRT_014_004:cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H_202112": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SPC_L4_NRT_014_004:cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H_202112"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-0.5deg_P1D-i_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-0.5deg_P1D-i_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D_202211": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D_202211"}, "EO:MO:DAT:WIND_GLO_PHY_CLIMATE_L4_MY_012_003:cmems_obs-wind_glo_phy_my_l4_P1M_202411": {"collection": "EO:MO:DAT:WIND_GLO_PHY_CLIMATE_L4_MY_012_003:cmems_obs-wind_glo_phy_my_l4_P1M_202411"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L4_NRT_012_004:cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H_202207": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L4_NRT_012_004:cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H_202207"}}}} +{"cop_marine": {"product_types_config": {"ANTARCTIC_OMI_SI_extent": {"abstract": "**DEFINITION**\n\nEstimates of Antarctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Southern Hemisphere (excluding ice lakes) and from 1993 up to real time aiming to:\ni) obtain the Antarctic sea ice extent as expressed in millions of km squared (106 km2) to monitor both the large-scale variability and mean state and change.\nii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss/gain since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013)). For the Southern Hemisphere, these trends are calculated from the annual mean values.\nThe Antarctic sea ice extent used here is based on the \u201cmulti-product\u201d (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread.\n\n**CONTEXT**\n\nSea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). \nThe sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018).\n \n**CMEMS KEY FINDINGS**\n\nWith quasi regular highs and lows, the annual Antarctic sea ice extent shows large variability until several monthly record high in 2014 and record lows in 2017, 2018 and 2023. Since the year 1993, the Southern Hemisphere annual sea ice extent regularly alternates positive and negative trend. The period 1993-2023 have seen a slight decrease at a rate of -0.28*106km2 per decade. This represents a loss amount of -2.4% per decade of Southern Hemisphere sea ice extent during this period; with however large uncertainties. The last quarter of the year 2016 and years 2017 and 2018 experienced unusual losses of ice. The year 2023 is an exceptional year and its average has a strong impact on the whole time series.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00186\n\n**References:**\n\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Samuelsen et al., 2016: Sea Ice In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9, 2016, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* Samuelsen et al., 2018: Sea Ice. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, 2018, DOI: 10.1080/1755876X.2018.1489208.\n* Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M.Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 317\u2013382, doi:10.1017/CBO9781107415324.012.\n", "doi": "10.48670/moi-00186", "instrument": null, "keywords": "antarctic-omi-si-extent,coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Sea Ice Extent from Reanalysis"}, "ANTARCTIC_OMI_SI_extent_obs": {"abstract": "**DEFINITION**\n\nSea Ice Extent (SIE) is defined as the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean surface that is covered with sea ice. SIC is computed from Passive Microwave satellite observations since 1979. \nSIE is often reported with units of 106 km2 (millions square kilometers). The change in sea ice extent (trend) is expressed in millions of km squared per decade (106 km2/decade). In addition, trends are expressed relative to the 1979-2022 period in % per decade.\nThese trends are calculated (i) from the annual mean values; (ii) from the September values (winter ice loss); (iii) from February values (summer ice loss). The annual mean trend is reported on the key figure, the September (maximum extent) and February (minimum extent) values are reported in the text below.\nSIE includes all sea ice, except for lake and river ice.\nSee also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI).\n\n**CONTEXT**\n\nSea ice is frozen seawater that floats at the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea-ice cover is changing is essential for monitoring the health of the Earth (Meredith et al. 2019). \n\n**CMEMS KEY FINDINGS**\n\nSince 1979, there has been an overall slight increase of sea ice extent in the Southern Hemisphere but a sharp decrease was observed after 2016. Over the period 1979-2022, the annual rate amounts to +0.02 +/- 0.05 106 km2 per decade (+0.18% per decade). Winter (September) sea ice extent trend amounts to +0.06 +/- 0.05106 km2 per decade (+0.32% per decade). Summer (February) sea ice extent trend amounts to -0.01+/- 0.05 106 km2 per decade (-0.38% per decade). These trend estimates are hardly significant, which is in agreement with the IPCC SROCC, which has assessed that \u2018Antarctic sea ice extent overall has had no statistically significant trend (1979\u20132018) due to contrasting regional signals and large interannual variability (high confidence).\u2019 (IPCC, 2019). Both June and July 2022 had the lowest average sea ice extent values for these months since 1979. \n\n**Figure caption**\n\na) The seasonal cycle of Southern Hemisphere sea ice extent expressed in millions of km2 averaged over the period 1979-2022 (red), shown together with the seasonal cycle in the year 2022 (green), and b) time series of yearly average Southern Hemisphere sea ice extent expressed in millions of km2. Time series are based on satellite observations (SMMR, SSM/I, SSMIS) by EUMETSAT OSI SAF Sea Ice Index (v2.2) with R&D input from ESA CCI. Details on the product are given in the corresponding PUM for this OMI. The change of sea ice extent over the period 1979-2022 is expressed as a trend in millions of square kilometers per decade and is plotted with a dashed line on panel b).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00187\n\n**References:**\n\n* Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* Samuelsen, A., L.-A. Breivik, R.P. Raj, G. Garric, L. Axell, E. Olason (2016): Sea Ice. In: The Copernicus Marine Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00187", "instrument": null, "keywords": "antarctic-omi-si-extent-obs,coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1978-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Monthly Sea Ice Extent from Observations Reprocessing"}, "ARCTIC_ANALYSISFORECAST_BGC_002_004": {"abstract": "The operational TOPAZ5-ECOSMO Arctic Ocean system uses the ECOSMO biological model coupled online to the TOPAZ5 physical model (ARCTIC_ANALYSISFORECAST_PHY_002_001 product). It is run daily to provide 10 days of forecast of 3D biogeochemical variables ocean. The coupling is done by the FABM framework.\n\nCoupling to a biological ocean model provides a description of the evolution of basic biogeochemical variables. The output consists of daily mean fields interpolated onto a standard grid and 40 fixed levels in NetCDF4 CF format. Variables include 3D fields of nutrients (nitrate, phosphate, silicate), phytoplankton and zooplankton biomass, oxygen, chlorophyll, primary productivity, carbon cycle variables (pH, dissolved inorganic carbon and surface partial CO2 pressure in seawater) and light attenuation coefficient. Surface Chlorophyll-a from satellite ocean colour is assimilated every week and projected downwards using a modified Ardyna et al. (2013) method. A new 10-day forecast is produced daily using the previous day's forecast and the most up-to-date prognostic forcing fields.\nOutput products have 6.25 km resolution at the North Pole (equivalent to 1/8 deg) on a stereographic projection. See the Product User Manual for the exact projection parameters.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00003\n\n**References:**\n\n* Ardyna, M., Babin, M., Gosselin, M., Devred, E., B\u00e9langer, S., Matsuoka, A., and Tremblay, J.-\u00c9.: Parameterization of vertical chlorophyll a in the Arctic Ocean: impact of the subsurface chlorophyll maximum on regional, seasonal, and annual primary production estimates, Biogeosciences, 10, 4383\u20134404, https://doi.org/10.5194/bg-10-4383-2013, 2013.\n* Yumruktepe, V. \u00c7., Samuelsen, A., and Daewel, U.: ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic, Geosci. Model Dev., 15, 3901\u20133921, https://doi.org/10.5194/gmd-15-3901-2022, 2022.\n", "doi": "10.48670/moi-00003", "instrument": null, "keywords": "arctic-analysisforecast-bgc-002-004,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,sea-water-ph-reported-on-total-scale,sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_002_001": {"abstract": "The operational TOPAZ5 Arctic Ocean system uses the HYCOM model and a 100-member EnKF assimilation scheme. It is run daily to provide 10 days of forecast (average of 10 members) of the 3D physical ocean, including sea ice with the CICEv5.1 model; data assimilation is performed weekly to provide 7 days of analysis (ensemble average).\n\nOutput products are interpolated on a grid of 6 km resolution at the North Pole on a polar stereographic projection. The geographical projection follows these proj4 library parameters: \n\nproj4 = \"+units=m +proj=stere +lon_0=-45 +lat_0=90 +k=1 +R=6378273 +no_defs\" \n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00001\n\n**References:**\n\n* Sakov, P., Counillon, F., Bertino, L., Lis\u00e6ter, K. A., Oke, P. R. and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8(4), 633\u2013656, doi:10.5194/os-8-633-2012, 2012.\n* Melsom, A., Counillon, F., LaCasce, J. H. and Bertino, L.: Forecasting search areas using ensemble ocean circulation modeling, Ocean Dyn., 62(8), 1245\u20131257, doi:10.1007/s10236-012-0561-5, 2012.\n", "doi": "10.48670/moi-00001", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-analysisforecast-phy-002-001,arctic-ocean,coastal-marine-environment,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": "proprietary", "missionStartDate": "2021-07-05T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Physics Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011": {"abstract": "The Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, including the Canadian Archipelago and the Bering Sea. neXtSIM is forced with surface atmosphere forcings from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ5, the ARC MFC PHY NRT system (002_001a). neXtSIM runs daily, assimilating manual ice charts, sea ice thickness from CS2SMOS in winter and providing 9-day forecasts. The output variables are the ice concentrations, ice thickness, ice drift velocity, snow depths, sea ice type, sea ice age, ridge volume fraction and albedo, provided at hourly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n\n**DOI (product):** \n\nhttps://doi.org/10.48670/moi-00004\n\n**References:**\n\n* Williams, T., Korosov, A., Rampal, P., and \u00d3lason, E.: Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F, The Cryosphere, 15, 3207\u20133227, https://doi.org/10.5194/tc-15-3207-2021, 2021.\n", "doi": "10.48670/moi-00004", "instrument": null, "keywords": "arctic-analysisforecast-phy-ice-002-011,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,surface-snow-thickness,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Sea Ice Analysis and Forecast"}, "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015": {"abstract": "The Arctic Ocean Surface Currents Analysis and Forecast system uses the HYCOM model at 3 km resolution forced with tides at its lateral boundaries, surface winds sea level pressure from the ECMWF (European Centre for Medium-Range Weather Forecasts) and wave terms (Stokes-Coriolis drift, stress and parameterisation of mixing by Langmuir cells) from the Arctic wave forecast. HYCOM runs daily providing 10 days forecast. The output variables are the surface currents and sea surface heights, provided at 15 minutes frequency, which therefore include mesoscale signals (though without data assimilation so far), tides and storm surge signals. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00005", "doi": "10.48670/moi-00005", "instrument": null, "keywords": "arctic-analysisforecast-phy-tide-002-015,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-surface-elevation,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Tidal Analysis and Forecast"}, "ARCTIC_ANALYSIS_FORECAST_WAV_002_014": {"abstract": "The Arctic Ocean Wave Analysis and Forecast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) together with tidal currents and ice from the ARC MFC forecasts (Sea Ice concentration and thickness). WAM runs twice daily providing one hourly 10 days forecast and one hourly 5 days forecast. From the output variables the most commonly used are significant wave height, peak period and mean direction.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00002", "doi": "10.48670/moi-00002", "instrument": null, "keywords": "arctic-analysis-forecast-wav-002-014,arctic-ocean,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Wave Analysis and Forecast"}, "ARCTIC_MULTIYEAR_BGC_002_005": {"abstract": "The TOPAZ-ECOSMO reanalysis system assimilates satellite chlorophyll observations and in situ nutrient profiles. The model uses the Hybrid Coordinate Ocean Model (HYCOM) coupled online to a sea ice model and the ECOSMO biogeochemical model. It uses the Determinstic version of the Ensemble Kalman Smoother to assimilate remotely sensed colour data and nutrient profiles. Data assimilation, including the 80-member ensemble production, is performed every 8-days. Atmospheric forcing fields from the ECMWF ERA-5 dataset are used\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00006\n\n**References:**\n\n* Simon, E., Samuelsen, A., Bertino, L. and Mouysset, S.: Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter, J. Mar. Syst., 152, 1\u201317, doi:10.1016/j.jmarsys.2015.07.004, 2015.\n", "doi": "10.48670/moi-00006", "instrument": null, "keywords": "arctic-multiyear-bgc-002-005,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2007-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "ARCTIC_MULTIYEAR_PHY_002_003": {"abstract": "The current version of the TOPAZ system - TOPAZ4b - is nearly identical to the real-time forecast system run at MET Norway. It uses a recent version of the Hybrid Coordinate Ocean Model (HYCOM) developed at University of Miami (Bleck 2002). HYCOM is coupled to a sea ice model; ice thermodynamics are described in Drange and Simonsen (1996) and the elastic-viscous-plastic rheology in Hunke and Dukowicz (1997). The model's native grid covers the Arctic and North Atlantic Oceans, has fairly homogeneous horizontal spacing (between 11 and 16 km). 50 hybrid layers are used in the vertical (z-isopycnal). TOPAZ4b uses the Deterministic version of the Ensemble Kalman filter (DEnKF; Sakov and Oke 2008) to assimilate remotely sensed as well as temperature and salinity profiles. The output is interpolated onto standard grids and depths for convenience. Daily values are provided at all depths and surfaces momentum and heat fluxes are provided as well. Data assimilation, including the 100-member ensemble production, is performed weekly.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00007", "doi": "10.48670/moi-00007", "instrument": null, "keywords": "arctic-multiyear-phy-002-003,arctic-ocean,coastal-marine-environment,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1991-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Physics Reanalysis"}, "ARCTIC_MULTIYEAR_PHY_ICE_002_016": {"abstract": "The Arctic Sea Ice Reanalysis system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, from Bering Strait to the North Atlantic. neXtSIM is forced by reanalyzed surface atmosphere forcings (ERA5) from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ4b, the ARC MFC MYP system (002_003). neXtSIM assimilates satellite sea ice concentrations from Passive Microwave satellite sensors, and sea ice thickness from CS2SMOS in winter from October 2010 onwards. The output variables are sea ice concentrations (total, young ice, and multi-year ice), sea ice thickness, sea ice velocity, snow depth on sea ice, sea ice type, sea ice age, sea ice ridge volume fraction and sea ice albedo, provided at daily and monthly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00336\n\n**References:**\n\n* Williams, T., Korosov, A., Rampal, P., and \u00d3lason, E.: Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F, The Cryosphere, 15, 3207\u20133227, https://doi.org/10.5194/tc-15-3207-2021, 2021.\n", "doi": "10.48670/mds-00336", "instrument": null, "keywords": "arctic-multiyear-phy-ice-002-016,arctic-ocean,coastal-marine-environment,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Sea Ice Reanalysis"}, "ARCTIC_MULTIYEAR_WAV_002_013": {"abstract": "The Arctic Ocean Wave Hindcast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA5 reanalysis together with ice from the ARC MFC reanalysis (Sea Ice concentration and thickness). Additionally, in the North Atlantic area, surface winds are used from a 2.5km atmospheric hindcast system. From the output variables the most commonly used are significant wave height, peak period and mean direction.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00008", "doi": "10.48670/moi-00008", "instrument": null, "keywords": "arctic-multiyear-wav-002-013,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-thickness,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Wave Hindcast"}, "ARCTIC_OMI_SI_Transport_NordicSeas": {"abstract": "**DEFINITION**\n\nNet sea-ice volume and area transport through the openings Fram Strait between Spitsbergen and Greenland along 79\u00b0N, 20\u00b0W - 10\u00b0E (positive southward); northern Barents Sea between Svalbard and Franz Josef Land archipelagos along 80\u00b0N, 27\u00b0E - 60\u00b0E (positive southward); eastern Barents Sea between the Novaya Zemlya and Franz Josef Land archipelagos along 60\u00b0E, 76\u00b0N - 80\u00b0N (positive westward). For further details, see Lien et al. (2021).\n\n**CONTEXT**\n\nThe Arctic Ocean contains a large amount of freshwater, and the freshwater export from the Arctic to the North Atlantic influence the stratification, and, the Atlantic Meridional Overturning Circulation (e.g., Aagaard et al., 1985). The Fram Strait represents the major gateway for freshwater transport from the Arctic Ocean, both as liquid freshwater and as sea ice (e.g., Vinje et al., 1998). The transport of sea ice through the Fram Strait is therefore important for the mass balance of the perennial sea-ice cover in the Arctic as it represents a large export of about 10% of the total sea ice volume every year (e.g., Rampal et al., 2011). Sea ice export through the Fram Strait has been found to explain a major part of the interannual variations in Arctic perennial sea ice volume changes (Ricker et al., 2018). The sea ice and associated freshwater transport to the Barents Sea has been suggested to be a driving mechanism for the presence of Arctic Water in the northern Barents Sea, and, hence, the presence of the Barents Sea Polar Front dividing the Barents Sea into a boreal and an Arctic part (Lind et al., 2018). In recent decades, the Arctic part of the Barents Sea has been giving way to an increasing boreal part, with large implications for the marine ecosystem and harvestable resources (e.g., Fossheim et al., 2015).\n\n**CMEMS KEY FINDINGS**\n\nThe sea-ice transport through the Fram Strait shows a distinct seasonal cycle in both sea ice area and volume transport, with a maximum in winter. There is a slight positive trend in the volume transport over the last two and a half decades. In the Barents Sea, a strong reduction of nearly 90% in average sea-ice thickness has diminished the sea-ice import from the Polar Basin (Lien et al., 2021). In both areas, the Fram Strait and the Barents Sea, the winds governed by the regional patterns of atmospheric pressure is an important driving force of temporal variations in sea-ice transport (e.g., Aaboe et al., 2021; Lien et al., 2021).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00192\n\n**References:**\n\n* Aaboe S, Lind S, Hendricks S, Down E, Lavergne T, Ricker R. 2021. Sea-ice and ocean conditions surprisingly normal in the Svalbard-Barents Sea region after large sea-ice inflows in 2019. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 140-148\n* Aagaard K, Swift JH, Carmack EC. 1985. Thermohaline circulation in the Arctic Mediterranean seas. J Geophys Res. 90(C7), 4833-4846\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Clim Change. doi:10.1038/nclimate2647\n* Lien VS, Raj RP, Chatterjee S. 2021. Modelled sea-ice volume and area transport from the Arctic Ocean to the Nordic and Barents seas. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 10-17\n* Lind S, Ingvaldsen RB, Furevik T. 2018. Arctic warming hotspot in the northern Barents Sea linked to declining sea ice import. Nature Clim Change. doi:10.1038/s41558-018-0205-y\n* Rampal P, Weiss J, Dubois C, Campin J-M. 2011. IPCC climate models do not capture Arctic sea ice drift acceleration: Consequences in terms of projected sea ice thinning and decline. J Geophys Res. 116, C00D07. https://doi.org/10.1029/2011JC007110\n* Ricker R, Girard-Ardhuin F, Krumpen T, Lique C. 2018. Satellite-derived sea ice export and its impact on Arctic ice mass balance. Cryosphere. 12, 3017-3032\n* Vinje T, Nordlund N, Kvambekk \u00c5. 1998. Monitoring ice thickness in Fram Strait. J Geophys Res. 103(C5), 10437-10449\n", "doi": "10.48670/moi-00192", "instrument": null, "keywords": "arctic-ocean,arctic-omi-si-transport-nordicseas,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Sea Ice Area/Volume Transport in the Nordic Seas from Reanalysis"}, "ARCTIC_OMI_SI_extent": {"abstract": "**DEFINITION**\n\nEstimates of Arctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Northern Hemisphere (excluding ice lakes) and from 1993 up to the year 2019 aiming to:\ni) obtain the Arctic sea ice extent as expressed in millions of km square (106 km2) to monitor both the large-scale variability and mean state and change.\nii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013). These trends are calculated in three ways, i.e. (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss).\nThe Arctic sea ice extent used here is based on the \u201cmulti-product\u201d (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread.\n\n**CONTEXT**\n\nSea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification, in global and regional sea level rates and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). \nThe sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 1993 to 2023 the Arctic sea ice extent has decreased significantly at an annual rate of -0.57*106 km2 per decade. This represents an amount of -4.8 % per decade of Arctic sea ice extent loss over the period 1993 to 2023. Over the period 1993 to 2018, summer (September) sea ice extent loss amounts to -1.18*106 km2/decade (September values), which corresponds to -14.85% per decade. Winter (March) sea ice extent loss amounts to -0.57*106 km2/decade, which corresponds to -3.42% per decade. These values slightly exceed the estimates given in the AR5 IPCC assessment report (estimate up to the year 2012) as a consequence of continuing Northern Hemisphere sea ice extent loss. Main change in the mean seasonal cycle is characterized by less and less presence of sea ice during summertime with time. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00190\n\n**References:**\n\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Samuelsen et al., 2016: Sea Ice In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9, 2016, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* Samuelsen et al., 2018: Sea Ice. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, 2018, DOI: 10.1080/1755876X.2018.1489208.\n* Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M.Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 317\u2013382, doi:10.1017/CBO9781107415324.012.\n", "doi": "10.48670/moi-00190", "instrument": null, "keywords": "arctic-ocean,arctic-omi-si-extent,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea Ice Extent from Reanalysis"}, "ARCTIC_OMI_SI_extent_obs": {"abstract": "**DEFINITION**\n\nSea Ice Extent (SIE) is defined as the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean that is covered with sea ice. It is computed from Passive Microwave satellite observations since 1979. \nSIE is often reported with units of 106 km2 (millions square kilometers). The change in sea ice extent (trend) is expressed in millions of km squared per decade (106 km2/decade). In addition, trends are expressed relative to the 1979-2022 period in % per decade.\nThese trends are calculated (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss). The annual mean trend is reported on the key figure, the March and September values are reported in the text below.\nSIE includes all sea ice, but not lake or river ice.\nSee also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI).\n\n**CONTEXT**\n\nSea ice is frozen seawater that floats at the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea ice cover is changing is essential for monitoring the health of the Earth. Sea ice has a significant impact on ecosystems and Arctic communities, as well as economic activities such as fisheries, tourism, and transport (Meredith et al. 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince 1979, the Northern Hemisphere sea ice extent has decreased at an annual rate of -0.51 +/- 0.03106 km2 per decade (-4.41% per decade). Loss of sea ice extent during summer exceeds the loss observed during winter periods: Summer (September) sea ice extent loss amounts to -0.81 +/- 0.06 106 km2 per decade (-12.73% per decade). Winter (March) sea ice extent loss amounts to -0.39 +/- 0.03 106 km2 per decade (-2.55% per decade). These values are in agreement with those assessed in the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (Meredith et al. 2019, with estimates up until year 2018). September 2022 had the 11th lowest mean September sea ice extent. Sea ice extent in September 2012 is to date the record minimum Northern Hemisphere sea ice extent value since the beginning of the satellite record, followed by September values in 2020.\n\n**Figure caption**\n\na) The seasonal cycle of Northern Hemisphere sea ice extent expressed in millions of km2 averaged over the period 1979-2022 (red), shown together with the seasonal cycle in the year 2022 (green), and b) time series of yearly average Northern Hemisphere sea ice extent expressed in millions of km2. Time series are based on satellite observations (SMMR, SSM/I, SSMIS) by EUMETSAT OSI SAF Sea Ice Index (v2.2) with R&D input from ESA CCI. Details on the product are given in the corresponding PUM for this OMI. The change of sea ice extent over the period 1979-2022 is expressed as a trend in millions of square kilometers per decade and is plotted with a dashed line in panel b).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00191\n\n**References:**\n\n* Samuelsen, A., L.-A. Breivik, R.P. Raj, G. Garric, L. Axell, E. Olason (2016): Sea Ice. In: The Copernicus Marine Service Ocean State Report, issue 1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n", "doi": "10.48670/moi-00191", "instrument": null, "keywords": "arctic-ocean,arctic-omi-si-extent-obs,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1978-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Monthly Mean Sea Ice Extent from Observations Reprocessing"}, "ARCTIC_OMI_TEMPSAL_FWC": {"abstract": "**DEFINITION**\n\nEstimates of Arctic liquid Freshwater Content (FWC in meters) are obtained from integrated differences of the measured salinity and a reference salinity (set to 34.8) from the surface to the bottom per unit area in the Arctic region with a water depth greater than 500m as function of salinity (S), the vertical cell thickness of the dataset (dz) and the salinity reference (Sref). Waters saltier than the 34.8 reference are not included in the estimation. The regional FWC values from 1993 up to real time are then averaged aiming to:\n* obtain the mean FWC as expressed in cubic km (km3) \n* monitor the large-scale variability and change of liquid freshwater stored in the Arctic Ocean (i.e. the change of FWC in time).\n\n**CONTEXT**\n\nThe Arctic region is warming twice as fast as the global mean and its climate is undergoing unprecedented and drastic changes, affecting all the components of the Arctic system. Many of these changes affect the hydrological cycle. Monitoring the storage of freshwater in the Arctic region is essential for understanding the contemporary Earth system state and variability. Variations in Arctic freshwater can induce changes in ocean stratification. Exported southward downstream, these waters have potential future implications for global circulation and heat transport. \n\n**CMEMS KEY FINDINGS**\n\nSince 1993, the Arctic Ocean freshwater has experienced a significant increase of 423 \u00b1 39 km3/year. The year 2016 witnessed the highest freshwater content in the Artic since the last 24 years. Second half of 2016 and first half of 2017 show a substantial decrease of the FW storage. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00193\n\n**References:**\n\n* G. Garric, O. Hernandez, C. Bricaud, A. Storto, K. A. Peterson, H. Zuo, 2018: Arctic Ocean freshwater content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s70\u2013s72, DOI: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00193", "instrument": null, "keywords": "arctic-ocean,arctic-omi-tempsal-fwc,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Freshwater Content from Reanalysis"}, "BALTICSEA_ANALYSISFORECAST_BGC_003_007": {"abstract": "This Baltic Sea biogeochemical model product provides forecasts for the biogeochemical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Different datasets are provided. One with daily means and one with monthly means values for these parameters: nitrate, phosphate, chl-a, ammonium, dissolved oxygen, ph, phytoplankton, zooplankton, silicate, dissolved inorganic carbon, dissolved iron, dissolved cdom, hydrogen sulfide, and partial pressure of co2 at the surface. Instantaenous values for the Secchi Depth and light attenuation valid for noon (12Z) are included in the daily mean files/dataset. Additionally a third dataset with daily accumulated values of the netto primary production is available. The product is produced by the biogeochemical model ERGOM (Neumann et al, 2021) one way coupled to a Baltic Sea set up of the NEMO ocean model, which provides the Baltic Sea physical ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). This biogeochemical product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The product covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00009", "doi": "10.48670/moi-00009", "instrument": null, "keywords": "baltic-sea,balticsea-analysisforecast-bgc-003-007,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "BALTICSEA_ANALYSISFORECAST_PHY_003_006": {"abstract": "This Baltic Sea physical model product provides forecasts for the physical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Several datasets are provided: One with hourly instantaneous values, one with daily mean values and one with monthly mean values, all containing these parameters: sea level variations, ice concentration and thickness at the surface, and temperature, salinity and horizontal and vertical velocities for the 3D field. Additionally a dataset with 15 minutes (instantaneous) surface values are provided for the sea level variation and the surface horizontal currents, as well as detided daily values. The product is produced by a Baltic Sea set up of the NEMOv4.2.1 ocean model. This product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The ocean model is forced with Stokes drift data from the Baltic Sea Wave forecast product (BALTICSEA_ANALYSISFORECAST_WAV_003_010). Satellite SST, sea ice concentrations and in-situ T and S profiles are assimilated into the model's analysis field.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00010", "doi": "10.48670/moi-00010", "instrument": null, "keywords": "baltic-sea,balticsea-analysisforecast-phy-003-006,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Physics Analysis and Forecast"}, "BALTICSEA_ANALYSISFORECAST_WAV_003_010": {"abstract": "This Baltic Sea wave model product provides forecasts for the wave conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Data are provided with hourly instantaneous data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the Stokes drift, and two paramters for the maximum wave. The product is based on the wave model WAM cycle 4.7. The wave model is forced with surface currents, sea level anomaly and ice information from the Baltic Sea ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00011", "doi": "10.48670/moi-00011", "instrument": null, "keywords": "baltic-sea,balticsea-analysisforecast-wav-003-010,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-12-01T01:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Wave Analysis and Forecast"}, "BALTICSEA_MULTIYEAR_BGC_003_012": {"abstract": "This Baltic Sea Biogeochemical Reanalysis product provides a biogeochemical reanalysis for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the biogeochemical model ERGOM one-way online-coupled with the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include nitrate, phosphate, ammonium, dissolved oxygen, ph, chlorophyll-a, secchi depth, surface partial co2 pressure and net primary production. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n**DOI (product):**\n\nhttps://doi.org/10.48670/moi-00012", "doi": "10.48670/moi-00012", "instrument": null, "keywords": "baltic-sea,balticsea-multiyear-bgc-003-012,cell-thickness,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Biogeochemistry Reanalysis"}, "BALTICSEA_MULTIYEAR_PHY_003_011": {"abstract": "This Baltic Sea Physical Reanalysis product provides a reanalysis for the physical conditions for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include sea level, ice concentration, ice thickness, salinity, temperature, horizonal velocities and the mixed layer depths. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00013", "doi": "10.48670/moi-00013", "instrument": null, "keywords": "baltic-sea,balticsea-multiyear-phy-003-011,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Physics Reanalysis"}, "BALTICSEA_MULTIYEAR_WAV_003_015": {"abstract": "**This product has been archived** \n\n\n\nThis Baltic Sea wave model multiyear product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1980 and up to 0.5-1 year compared to real time.\nThis hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the maximum waves, and also the Stokes drift. Another dataset contains hourly values for five air-sea flux parameters. Additionally a dataset with monthly climatology are provided for the significant wave height and the wave period. The product is based on the wave model WAM cycle 4.7, and surface forcing from ECMWF's ERA5 reanalysis products. The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00014", "doi": "10.48670/moi-00014", "instrument": null, "keywords": "baltic-sea,balticsea-multiyear-wav-003-015,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,surface-roughness-length,wave-momentum-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T01:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Wave Hindcast"}, "BALTICSEA_REANALYSIS_WAV_003_015": {"abstract": "This Baltic Sea wave model hindcast product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1993 and up to 0.5-1 year compared to real time.\nThis hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, and also Stokes drift. Additionally a dataset with monthly climatology are provided for the significant wave height and the wave period. The product is based on the wave model WAM cycle 4.6.2, and surface forcing from ECMWF's ERA5 reanalysis products. The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00014", "doi": "10.48670/moi-00014", "instrument": null, "keywords": "baltic-sea,balticsea-reanalysis-wav-003-015,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T01:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "FMI (Finland)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Wave Hindcast"}, "BALTIC_OMI_HEALTH_codt_volume": {"abstract": "**DEFINITION**\n\nThe cod reproductive volume has been derived from regional reanalysis modelling results for the Baltic Sea BALTICSEA_MULTIYEAR_PHY_003_011 and BALTICSEA_MULTIYEAR_BGC_003_012. The volume has been calculated taking into account the three most important influencing abiotic factors of cod reproductive success: salinity > 11 g/kg, oxygen concentration\u2009>\u20092 ml/l and water temperature over 1.5\u00b0C (MacKenzie et al., 1996; Heikinheimo, 2008; Plikshs et al., 2015). The daily volumes are calculated as the volumes of the water with salinity > 11 g/kg, oxygen content\u2009>\u20092 ml/l and water temperature over 1.5\u00b0C in the Baltic Sea International Council for the Exploration of the Sea subdivisions of 25-28 (ICES, 2019).\n\n**CONTEXT**\n\nCod (Gadus morhua) is a characteristic fish species in the Baltic Sea with major economic importance. Spawning stock biomasses of the Baltic cod have gone through a steep decline in the late 1980s (Bryhn et al., 2022). Water salinity and oxygen concentration affect cod stock through the survival of eggs (Westin and Nissling, 1991; Wieland et al., 1994). Major Baltic Inflows provide a suitable environment for cod reproduction by bringing saline oxygenated water to the deep basins of the Baltic Sea (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland). Increased cod reproductive volume has a positive effect on cod reproduction success, which should reflect an increase of stock size indicator 4\u20135 years after the Major Baltic Inflow (Raudsepp et al., 2019). Eastern Baltic cod reaches maturity around age 2\u20133, depending on the population density and environmental conditions. Low oxygen and salinity cause stress, which negatively affects cod recruitment, whereas sufficient conditions may bring about male cod maturation even at the age of 1.5 years (Cardinale and Modin, 1999; Karasiova et al., 2008). There are a number of environmental factors affecting cod populations (Bryhn et al., 2022).\n\n**KEY FINDINGS**\n\nTypically, the cod reproductive volume in the Baltic Sea oscillates between 200 and 400 km\u00b3. There have been two distinct periods of significant increase, with maximum values reaching over 1200 km\u00b3, corresponding to the aftermath of Major Baltic Inflows (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland) from 2003 to 2004 and from 2016 to 2017. Following a decline to the baseline of 200 km\u00b3 in 2018, there was a rise to 800 km\u00b3 in 2019. The cod reproductive volume hit a second peak of 800 km\u00b3 in 2022 and has since stabilized at 600 km\u00b3. However, Bryhn et al. (2022) report no observed increase in the spawning stock biomass of the eastern Baltic Sea cod.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00196\n\n**References:**\n\n* Cardinale, M., Modin, J., 1999. Changes in size-at-maturity of Baltic cod (Gadus morhua) during a period of large variations in stock size and environmental conditions. Vol. 41 (3), 285-295. https://doi.org/10.1016/S0165-7836(99)00021-1\n* Heikinheimo, O., 2008. Average salinity as an index for environmental forcing on cod recruitment in the Baltic Sea. Boreal Environ Res 13:457\n* ICES, 2019. Baltic Sea Ecoregion \u2013 Fisheries overview, ICES Advice, DOI:10.17895/ices.advice.5566 Karasiova, E.M., Voss, R., Eero, M., 2008. Long-term dynamics in eastern Baltic cod spawning time: from small scale reversible changes to a recent drastic shift. ICES CM 2008/J:03\n* MacKenzie, B., St. John, M., Wieland, K., 1996. Eastern Baltic cod: perspectives from existing data on processes affecting growth and survival of eggs and larvae. Mar Ecol Prog Ser Vol. 134: 265-281.\n* Plikshs, M., Hinrichsen, H. H., Elferts, D., Sics, I., Kornilovs, G., K\u00f6ster, F., 2015. Reproduction of Baltic cod, Gadus morhua (Actinopterygii: Gadiformes: Gadidae), in the Gotland Basin: Causes of annual variability. Acta Ichtyologica et Piscatoria, Vol. 45, No. 3, 2015, p. 247-258.\n* Raudsepp, U., Legeais, J.-F., She, J., Maljutenko, I., Jandt, S., 2018. Baltic inflows. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s106\u2013s110, DOI: 10.1080/1755876X.2018.1489208\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, s26\u2013s30; DOI: 10.1080/ 1755876X.2019.1633075\n* Westin, L., Nissling, A., 1991. Effects of salinity on spermatozoa motility, percentage of fertilized eggs and egg development of Baltic cod Gadus morhua, and implications for cod stock fluctuations in the Baltic. Mar. Biol. 108, 5 \u2013 9.\n* Wieland, K., Waller, U., Schnack, D., 1994. Development of Baltic cod eggs at different levels of temperature and oxygen content. Dana 10, 163 \u2013 177.\n* Bryhn, A.C.., Bergek, S., Bergstr\u00f6m,U., Casini, M., Dahlgren, E., Ek, C., Hjelm, J., K\u00f6nigson, S., Ljungberg, P., Lundstr\u00f6m, K., Lunneryd, S.G., Oveg\u00e5rd, M., Sk\u00f6ld, M., Valentinsson, D., Vitale, F., Wennhage, H., 2022. Which factors can affect the productivity and dynamics of cod stocks in the Baltic Sea, Kattegat and Skagerrak? Ocean & Coastal Management, 223, 106154. https://doi.org/10.1016/j.ocecoaman.2022.106154\n", "doi": "10.48670/moi-00196", "instrument": null, "keywords": "baltic-omi-health-codt-volume,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-volume,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Cod Reproductive Volume from Reanalysis"}, "BALTIC_OMI_OHC_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe method for calculating the ocean heat content anomaly is based on the daily mean sea water potential temperature fields (Tp) derived from the Baltic Sea reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The total heat content is determined using the following formula:\n\nHC = \u03c1 * cp * ( Tp +273.15).\n\nHere, \u03c1 and cp represent spatially varying sea water density and specific heat, respectively, which are computed based on potential temperature, salinity and pressure using the UNESCO 1983 polynomial developed by Fofonoff and Millard (1983). The vertical integral is computed using the static cell vertical thicknesses sourced from the reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011 dataset cmems_mod_bal_phy_my_static, spanning from the sea surface to the 300 m depth. Spatial averaging is performed over the Baltic Sea spatial domain, defined as the region between 13\u00b0 - 31\u00b0 E and 53\u00b0 - 66\u00b0 N. To obtain the OHC annual anomaly time series in (J/m2), the mean heat content over the reference period of 1993-2014 was subtracted from the annual mean total heat content.\nWe evaluate the uncertainty from the mean annual error of the potential temperature compared to the observations from the Baltic Sea (Giorgetti et al., 2020). The shade corresponds to the RMSD of the annual mean heat content biases (\u00b1 35.3 MJ/m\u00b2) evaluated from the observed temperatures and corresponding model values. \nLinear trend (W/m2) has been estimated from the annual anomalies with the uncertainty of 1.96-times standard error.\n\n**CONTEXT**\n\nOcean heat content is a key ocean climate change indicator. It accounts for the energy absorbed and stored by oceans. Ocean Heat Content in the upper 2,000 m of the World Ocean has increased with the rate of 0.35 \u00b1 0.08 W/m2 in the period 1955\u20132019, while during the last decade of 2010\u20132019 the warming rate was 0.70 \u00b1 0.07 W/m2 (Garcia-Soto et al., 2021). The high variability in the local climate of the Baltic Sea region is attributed to the interplay between a temperate marine zone and a subarctic continental zone. Therefore, the Ocean Heat Content of the Baltic Sea could exhibit strong interannual variability and the trend could be less pronounced than in the ocean.\n\n**KEY FINDINGS**\n\nThe ocean heat content (OHC) of the Baltic Sea exhibits an increasing trend of 0.3\u00b10.1 W/m\u00b2, along with multi-year oscillations. This increase is less pronounced than the global OHC trend (Holland et al. 2019; von Schuckmann et al. 2019) and that of some other marginal seas (von Schuckmann et al. 2018; Lima et al. 2020). The relatively low trend values are attributed to the Baltic Sea's shallowness, which constrains heat accumulation in its waters. The most significant OHC anomaly was recorded in 2020, and following a decline from this peak, the anomaly has now stabilized at approximately 250 MJ/m\u00b2.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00322\n\n**References:**\n\n* Garcia-Soto C, Cheng L, Caesar L, Schmidtko S, Jewett EB, Cheripka A, Rigor I, Caballero A, Chiba S, B\u00e1ez JC, Zielinski T and Abraham JP (2021) An Overview of Ocean Climate Change Indicators: Sea Surface Temperature, Ocean Heat Content, Ocean pH, Dissolved Oxygen Concentration, Arctic Sea Ice Extent, Thickness and Volume, Sea Level and Strength of the AMOC (Atlantic Meridional Overturning Circulation). Front. Mar. Sci. 8:642372. doi: 10.3389/fmars.2021.642372\n* Fofonoff, P. and Millard, R.C. Jr UNESCO 1983. Algorithms for computation of fundamental properties of seawater. UNESCO Tech. Pap. in Mar. Sci., No. 44, 53 pp., p.39. http://unesdoc.unesco.org/images/0005/000598/059832eb.pdf\n* Giorgetti, A., Lipizer, M., Molina Jack, M.E., Holdsworth, N., Jensen, H.M., Buga, L., Sarbu, G., Iona, A., Gatti, J., Larsen, M. and Fyrberg, L., 2020. Aggregated and Validated Datasets for the European Seas: The Contribution of EMODnet Chemistry. Frontiers in Marine Science, 7, p.583657.\n* Holland E, von Schuckmann K, Monier M, Legeais J-F, Prado S, Sathyendranath S, Dupouy C. 2019. The use of Copernicus Marine Service products to describe the state of the tropical western Pacific Ocean around the islands: a case study. In: Copernicus Marine Service Ocean State Report, Issue 3. J Oper Oceanogr. 12(suppl. 1):s43\u2013s48. doi:10.1080/1755876X.2019.1633075\n* Lima L, Peneva E, Ciliberti S, Masina S, Lemieux B, Storto A, Chtirkova B. 2020. Ocean heat content in the Black Sea. In: Copernicus Marine Service Ocean State Report, Issue 4. J Oper Oceanogr. 13(suppl. 1):s41\u2013s48. doi:10.1080/1755876X.2020.1785097.\n* von Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G. 2019. Copernicus Marine Service Ocean State report. J Oper Oceanogr. 12(suppl. 1):s1\u2013s123. doi:10.1080/1755876X.2019.1633075.\n* von Schuckmann K, Storto A, Simoncelli S, Raj RP, Samuelsen A, Collar A, Sotillo MG, Szerkely T, Mayer M, Peterson KA, et al. 2018. Ocean heat content. In: Copernicus Marine Service Ocean State Report, issue 2. J Oper Oceanogr. 11 (Suppl. 1):s1\u2013s142. doi:10.1080/1755876X.2018.1489208.\n", "doi": "10.48670/mds-00322", "instrument": null, "keywords": "baltic-omi-ohc-area-averaged-anomalies,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,ohc-balrean,sea-water-salinity,sea-water-temperature,volume-fraction-of-oxygen-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ocean Heat Content Anomaly (0-300m) from Reanalysis"}, "BALTIC_OMI_SI_extent": {"abstract": "**DEFINITION**\n\nSea ice extent is defined as the area covered by sea ice, that is the area of the ocean having more than 15% sea ice concentration. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. Daily sea ice extent values are computed from the daily sea ice concentration maps. All sea ice covering the Baltic Sea is included, except for lake ice. The data used to produce the charts are Synthetic Aperture Radar images as well as in situ observations from ice breakers (Uiboupin et al., 2010). The annual course of the sea ice extent has been calculated as daily mean ice extent for each day-of-year over the period October 1992 \u2013 September 2014. Weekly smoothed time series of the sea ice extent have been calculated from daily values using a 7-day moving average filter.\n\n**CONTEXT**\n\nSea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Moreover, it is an important parameter for safe winter navigation. The presence of sea ice cover sets special requirements for navigation, both for the construction of the ships and their behavior in ice, as in many cases, merchant ships need icebreaker assistance. Temporal trends of the sea ice extent could be a valuable indicator of the climate change signal in the Baltic Sea region. It has been estimated that a 1 \u00b0C increase in the average air temperature results in the retreat of ice-covered area in the Baltic Sea about 45,000 km2 (Granskog et al., 2006). Decrease in maximum ice extent may influence vertical stratification of the Baltic Sea (Hordoir and Meier, 2012) and affect the onset of the spring bloom (Eilola et al., 2013). In addition, statistical sea ice coverage information is crucial for planning of coastal and offshore construction. Therefore, the knowledge about ice conditions and their variability is required and monitored in Copernicus Marine Service.\n\n**KEY FINDINGS**\n\nSea ice in the Baltic Sea exhibits a strong seasonal pattern. Typically, formation begins in October and can persist until June. The 2022/23 ice season saw a relatively low maximum ice extent in the Baltic Sea, peaking at around 65,000 km\u00b2. Formation started in November and accelerated in the second week of December. The ice extent then remained fairly stable and below the climatological average until the end of January. From February to the second week of March, the extent of sea ice steadily grew to its maximum of 65,000 km\u00b2, before gradually receding. The peak day for sea ice extent varies annually but generally oscillates between the end of February and the start of March (Singh et al., 2024). The past 30 years saw the highest sea ice extent at 260,000 km\u00b2 in 2010/11. Despite a downward trend in sea ice extent in the Baltic Sea from 1993 to 2022, the linear trend does not show statistical significance.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00200\n\n**References:**\n\n* Eilola K, M\u00e5rtensson S, Meier HEM, 2013. Modeling the impact of reduced sea ice cover in future climate on the Baltic Sea biogeochemistry. Geophysical Research Letters, 40, 149-154, doi:10.1029/2012GL054375\n* Granskog M, Kaartokallio H, Kuosa H, Thomas DN, Vainio J, 2006. Sea ice in the Baltic Sea \u2013 A review. Estuarine, Coastal and Shelf Science, 70, 145\u2013160. doi:10.1016/j.ecss.2006.06.001\n* Hordoir R., Meier HEM, 2012. Effect of climate change on the thermal stratification of the Baltic Sea: a sensitivity experiment. Climate Dynamics, 38, 1703-1713, doi:10.1007/s00382-011-1036-y\n* Uiboupin R, Axell L, Raudsepp U, Sipelgas L, 2010. Comparison of operational ice charts with satellite based ice concentration products in the Baltic Sea. 2010 IEEE/ OES US/EU Balt Int Symp Balt 2010, doi:10.1109/BALTIC.2010.5621649\n* Vihma T, Haapala J, 2009. Geophysics of sea ice in the Baltic Sea: A review. Progress in Oceanography, 80, 129-148, doi:10.1016/j.pocean.2009.02.002\n", "doi": "10.48670/moi-00200", "instrument": null, "keywords": "baltic-omi-si-extent,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-extent,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ice Extent from Observations Reprocessing"}, "BALTIC_OMI_SI_volume": {"abstract": "**DEFINITION**\n\nThe sea ice volume is a product of sea ice concentration and sea ice thickness integrated over respective area. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. The Baltic Sea area having more than 15% of sea ice concentration is included into the sea ice volume analysis. Daily sea ice volume values are computed from the daily sea ice concentration and sea ice thickness maps. The data used to produce the charts are Synthetic Aperture Radar images as well as in situ observations from ice breakers (Uiboupin et al., 2010; https://www.smhi.se/data/oceanografi/havsis). The annual course of the sea ice volume has been calculated as daily mean ice volume for each day-of-year over the period October 1992 \u2013 September 2014. Weekly smoothed time series of the sea ice volume have been calculated from daily values using a 7-day moving average filter.\n\n**CONTEXT**\n\nSea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Knowledge of the sea ice volume facilitates planning of icebreaking activity and operation of the icebreakers (Valdez Banda et al., 2015; Bostr\u00f6m and \u00d6sterman, 2017). A long-term monitoring of ice parameters is required for design and installation of offshore constructions in seasonally ice covered seas (Heinonen and Rissanen, 2017). A reduction of the sea ice volume in the Baltic Sea has a critical impact on the population of ringed seals (Harkonen et al., 2008). Ringed seals need stable ice conditions for about two months for breeding and moulting (Sundqvist et al., 2012). The sea ice is a habitat for diverse biological assemblages (Enberg et al., 2018).\n\n**KEY FINDINGS**\n\nIn the Baltic Sea, the ice season may begin in October and last until June. The maximum sea ice volume typically peaks in March, but in 2023, it was observed in April. The 2022/23 ice season saw a low maximum sea ice volume of approximately 14 km\u00b3. From 1993 to 2023, the annual maximum ice volume ranged from 4 km\u00b3 in 2020 to 60 km\u00b3 in 1996. There is a statistically significant downward trend of -0.73 km\u00b3/year (p=0.01) in the Baltic Sea's maximum sea ice volume. Recent trends indicate a decrease in sea ice fraction and thickness across the Baltic Sea, particularly in the Bothnian Bay, as reported by Singh et al. (2024).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00201\n\n**References:**\n\n* Bostr\u00f6m M, \u00d6sterman C, 2017, Improving operational safety during icebreaker operations, WMU Journal of Maritime Affairs, 16, 73-88, DOI: 10.1007/s13437-016-0105-9\n* Enberg S, Majaneva M, Autio R, Blomster J, Rintala J-M, 2018, Phases of microalgal succession in sea ice and the water column in the baltic sea from autumn to spring. Marine Ecology Progress Series, 559, 19-34. DOI: 10.3354/meps12645\n* Harkonen T, J\u00fcssi M, J\u00fcssi I, Verevkin M, Dmitrieva L, Helle E, Sagitov R, Harding KC, 2008, Seasonal Activity Budget of Adult Baltic Ringed Seals, PLoS ONE 3(4): e2006, DOI: 0.1371/journal.pone.0002006\n* Heinonen J, Rissanen S, 2017, Coupled-crushing analysis of a sea ice - wind turbine interaction \u2013 feasibility study of FAST simulation software, Ships and Offshore Structures, 12, 1056-1063. DOI: 10.1080/17445302.2017.1308782\n* Sundqvist L, Harkonen T, Svensson CJ, Harding KC, 2012, Linking Climate Trends to Population Dynamics in the Baltic Ringed Seal: Impacts of Historical and Future Winter Temperatures, AMBIO, 41: 865, DOI: 10.1007/s13280-012-0334-x\n* Uiboupin R, Axell L, Raudsepp U, Sipelgas L, 2010, Comparison of operational ice charts with satellite based ice concentration products in the Baltic Sea. 2010 IEEE/ OES US/EU Balt Int Symp Balt 2010, DOI: 10.1109/BALTIC.2010.5621649\n* Valdez Banda OA, Goerlandt F, Montewka J, Kujala P, 2015, A risk analysis of winter navigation in Finnish sea areas, Accident Analysis & Prevention, 79, 100\u2013116, DOI: 10.1016/j.aap.2015.03.024\n", "doi": "10.48670/moi-00201", "instrument": null, "keywords": "baltic-omi-si-volume,baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-volume,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ice Volume from Observations Reprocessing"}, "BALTIC_OMI_TEMPSAL_Stz_trend": {"abstract": "**DEFINITION**\n\nThe subsurface salinity trends have been derived from regional reanalysis and forecast modelling results of the Copernicus Marine Service BAL MFC group for the Baltic Sea (product reference BALTICSEA_MULTIYEAR_PHY_003_011). The salinity trend has been obtained through a linear fit for each time series of horizontally averaged (13 \u00b0E - 31 \u00b0E and 53 \u00b0N - 66 \u00b0N; excluding the Skagerrak strait) annual salinity and at each depth level.\n\n**CONTEXT**\n\nThe Baltic Sea is a brackish semi-enclosed sea in North-Eastern Europe. The surface salinity varies horizontally from ~10 near the Danish Straits down to ~2 at the northernmost and easternmost sub-basins of the Baltic Sea. The halocline, a vertical layer with rapid changes of salinity with depth that separates the well-mixed surface layer from the weakly stratified layer below, is located at the depth range of 60-80 metres (Matth\u00e4us, 1984). The bottom layer salinity below the halocline depth varies from 15 in the south down to 3 in the northern Baltic Sea (V\u00e4li et al., 2013). The long-term salinity is determined by net precipitation and river discharge as well as saline water inflows from the North Sea (Lehmann et al., 2022). Long-term salinity decrease may reduce the occurrence and biomass of the Fucus vesiculosus - Idotea balthica association/symbiotic aggregations (Kotta et al., 2019). Changes in salinity and oxygen content affect the survival of the Baltic cod eggs (Raudsepp et al, 2019; von Dewitz et al., 2018).\n\n**KEY FINDINGS**\n\nThe subsurface salinity from 1993 to 2023 exhibits distinct variations at different depths. In the surface layer up to 25 meters, which aligns with the average upper mixed layer depth in the Baltic Sea, there is no discernible trend. The salinity trend increases steadily from zero at a 25-meter depth to 0.04 per year at 70 meters. The most pronounced trend, 0.045 per year, is found within the extended halocline layer ranging from 70 to 150 meters. It is noteworthy that there is a slight reduction in the salinity trend to 0.04 per year between depths of 150 and 220 meters. Although this decrease is minor, it suggests that salt transport into the extended halocline layer is more pronounced than into the deeper layers. The Major Baltic Inflows are responsible for the significant salinity trend of 0.05 per year observed in the deepest layer of the Baltic Sea. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00207\n\n**References:**\n\n* von Dewitz B, Tamm S, Ho\u00c8flich K, Voss R, Hinrichsen H-H., 2018. Use of existing hydrographic infrastructure to forecast the environmental spawning conditions for Eastern Baltic cod, PLoS ONE 13(5): e0196477, doi:10.1371/journal.pone.0196477\n* Kotta, J., Vanhatalo, J., J\u00e4nes, H., Orav-Kotta, H., Rugiu, L., Jormalainen, V., Bobsien, I., Viitasalo, M., Virtanen, E., Nystr\u00f6m Sandman, A., Isaeus, M., Leidenberger, S., Jonsson, P.R., Johannesson, K., 2019. Integrating experimental and distribution data to predict future species patterns. Scientific Reports, 9: 1821, doi:10.1038/s41598-018-38416-3\n* Matth\u00e4us W, 1984, Climatic and seasonal variability of oceanological parameters in the Baltic Sea, Beitr. Meereskund, 51, 29\u201349.\n* Sandrine Mulet, Bruno Buongiorno Nardelli, Simon Good, Andrea Pisano, Eric Greiner, Maeva Monier, Emmanuelle Autret, Lars Axell, Fredrik Boberg, Stefania Ciliberti, Marie Dr\u00e9villon, Riccardo Droghei, Owen Embury, J\u00e9rome Gourrion, Jacob H\u00f8yer, M\u00e9lanie Juza, John Kennedy, Benedicte Lemieux-Dudon, Elisaveta Peneva, Rebecca Reid, Simona Simoncelli, Andrea Storto, Jonathan Tinker, Karina von Schuckmann and Sarah L. Wakelin. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI:10.1080/1755876X.2018.1489208\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. 2.7 Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3\n* V\u00e4li G, Meier HEM, Elken J, 2013, Simulated halocline variability in the baltic sea and its impact on hypoxia during 1961-2007, Journal of Geophysical Research: Oceans, 118(12), 6982\u20137000, DOI:10.1002/2013JC009192\n", "doi": "10.48670/moi-00207", "instrument": null, "keywords": "baltic-omi-tempsal-stz-trend,baltic-sea,coastal-marine-environment,confidence-interval,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-salinity-trend,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Subsurface Salinity trend from Reanalysis"}, "BALTIC_OMI_TEMPSAL_Ttz_trend": {"abstract": "**DEFINITION**\n\nThe subsurface temperature trends have been derived from regional reanalysis results for the Baltic Sea (product references BALTICSEA_MULTIYEAR_PHY_003_011). Horizontal averaging has been done over the Baltic Sea domain (13 \u00b0E - 31 \u00b0E and 53 \u00b0N - 66 \u00b0N; excluding the Skagerrak strait). The temperature trend has been obtained through a linear fit for each time series of horizontally averaged annual temperature and at each depth level. \n\n**CONTEXT**\n\nThe Baltic Sea is a semi-enclosed sea in North-Eastern Europe. The temperature of the upper mixed layer of the Baltic Sea is characterised by a strong seasonal cycle driven by the annual course of solar radiation (Lepp\u00e4ranta and Myrberg, 2008). The maximum water temperatures in the upper layer are reached in July and August and the minimum during February, when the Baltic Sea becomes partially frozen (CMEMS OMI Baltic Sea Sea Ice Extent, CMEMS OMI Baltic Sea Sea Ice Volume). Seasonal thermocline, developing in the depth range of 10-30 m in spring, reaches its maximum strength in summer and is eroded in autumn. During autumn and winter the Baltic Sea is thermally mixed down to the permanent halocline in the depth range of 60-80 metres (Matth\u00e4us, 1984). The 20\u201350\u202fm thick cold intermediate layer forms below the upper mixed layer in March and is observed until October within the 15-65 m depth range (Chubarenko and Stepanova, 2018; Liblik and Lips, 2011). The deep layers of the Baltic Sea are disconnected from the ventilated upper ocean layers, and temperature variations are predominantly driven by mixing processes and horizontal advection. A warming trend of the sea surface waters is positively correlated with the increasing trend of diffuse attenuation of light (Kd490) and satellite-detected chlorophyll concentration (Kahru et al., 2016). Temperature increase in the water column could accelerate oxygen consumption during organic matter oxidation (Savchuk, 2018).\n\n**KEY FINDINGS**\n\nAnalysis of subsurface temperatures from 1993 to 2023 indicates that the Baltic Sea is experiencing warming across all depth intervals. The temperature trend in the upper mixed layer (0-25 m) is approximately 0.055 \u00b0C/year, decreasing to 0.045 \u00b0C/year within the seasonal thermocline layer. A peak temperature trend of 0.065 \u00b0C/year is observed at a depth of 70 m, aligning with the base of the cold intermediate layer. Beyond this depth, the trend stabilizes, closely matching the 0.065 \u00b0C/year value. At a 95% confidence level, it can be stated that the Baltic Sea's warming is consistent with depth, averaging around 0.06 \u00b0C/year. Notably, recent trends show a significant increase; for instance, Savchuk's 2018 measurements indicate an average temperature trend of 0.04 \u00b0C/year in the Baltic Proper's deep layers (>60m) from 1979 to 2016.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00208\n\n**References:**\n\n* Chubarenko, I., Stepanova, N. 2018. Cold intermediate layer of the Baltic Sea: Hypothesis of the formation of its core. Progress in Oceanography, 167, 1-10, doi: 10.1016/j.pocean.2018.06.012\n* Kahru, M., Elmgren, R., and Savchuk, O. P. 2016. Changing seasonality of the Baltic Sea. Biogeosciences 13, 1009\u20131018. doi: 10.5194/bg-13-1009-2016\n* Lepp\u00e4ranta, M., Myrberg, K. 2008. Physical Oceanography of the Baltic Sea. Springer, Praxis Publishing, Chichester, UK, pp. 370\n* Liblik, T., Lips, U. 2011. Characteristics and variability of the vertical thermohaline structure in the Gulf of Finland in summer. Boreal Environment Research, 16, 73-83.\n* Matth\u00e4us W, 1984, Climatic and seasonal variability of oceanological parameters in the Baltic Sea, Beitr. Meereskund, 51, 29\u201349.\n* Savchuk, .P. 2018. Large-Scale Nutrient Dynamics in the Baltic Sea, 1970\u20132016. Frontiers in Marine Science, 5:95, doi: 10.3389/fmars.2018.00095\n", "doi": "10.48670/moi-00208", "instrument": null, "keywords": "baltic-omi-tempsal-ttz-trend,baltic-sea,coastal-marine-environment,confidence-interval,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-temperature-trend,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Subsurface Temperature trend from Reanalysis"}, "BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm": {"abstract": "**DEFINITION**\n\nMajor Baltic Inflows bring large volumes of saline and oxygen-rich water into the bottom layers of the deep basins of the Baltic Sea- Bornholm basin, Gdansk basin and Gotland basin. The Major Baltic Inflows occur seldom, sometimes many years apart (Mohrholz, 2018). The Major Baltic Inflow OMI consists of the time series of the bottom layer salinity in the Arkona basin and in the Bornholm basin and the time-depth plot of temperature, salinity and dissolved oxygen concentration in the Gotland basin (BALTIC_OMI_WMHE_mbi_sto2tz_gotland). Bottom salinity increase in the Arkona basin is the first indication of the saline water inflow, but not necessarily Major Baltic Inflow. Abrupt increase of bottom salinity of 2-3 units in the more downstream Bornholm basin is a solid indicator that Major Baltic Inflow has occurred.\nThe subsurface temperature trends have been derived from regional reanalysis results for the Baltic \n\n**CONTEXT**\n\nThe Baltic Sea is a huge brackish water basin in Northern Europe whose salinity is controlled by its freshwater budget and by the water exchange with the North Sea (e.g. Neumann et al., 2017). The saline and oxygenated water inflows to the Baltic Sea through the Danish straits, especially the Major Baltic Inflows, occur only intermittently (e.g. Mohrholz, 2018). Long-lasting periods of oxygen depletion in the deep layers of the central Baltic Sea accompanied by a salinity decline and the overall weakening of vertical stratification are referred to as stagnation periods. Extensive stagnation periods occurred in the 1920s/1930s, in the 1950s/1960s and in the 1980s/beginning of 1990s Lehmann et al., 2022). Bottom salinity variations in the Arkona Basin represent water exchange between the Baltic Sea and Skagerrak-Kattegat area. The increasing salinity signal in that area does not indicate that a Major Baltic Inflow has occurred. The mean sea level of the Baltic Sea derived from satellite altimetry data can be used as a proxy for the detection of saline water inflows to the Baltic Sea from the North Sea (Raudsepp et al., 2018). The medium and strong inflow events increase oxygen concentration in the near-bottom layer of the Bornholm Basin while some medium size inflows have no impact on deep water salinity (Mohrholz, 2018). \n\n**KEY FINDINGS**\n\nTime series data of bottom salinity variations in the Arkona Basin are instrumental for monitoring the sporadic nature of water inflow and outflow events. The bottom salinity in the Arkona Basin fluctuates between 11 and 25 g/kg. The highest recorded bottom salinity value is associated with the Major Baltic Inflow of 2014, while other significant salinity peaks align with the Major Baltic Inflows of 1993 and 2002. Low salinity episodes in the Arkona Basin mark the occasions of barotropic outflows of brackish water from the Baltic Sea. In the Bornholm Basin, the bottom salinity record indicates three Major Baltic Inflow events: the first in 1993, followed by 2002, and the most recent in 2014. Following the last Major Baltic Inflow, the bottom salinity in the Bornholm Basin rose to 20 g/kg. Over the subsequent nine years, it has declined to 16 g/kg. The winter of 2023/24 did not experience a Major Baltic Inflow.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00209\n\n**References:**\n\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Mohrholz V, 2018, Major Baltic Inflow Statistics \u2013 Revised. Frontiers in Marine Science, 5:384, doi: 10.3389/fmars.2018.00384\n* Neumann, T., Radtke, H., Seifert, T., 2017. On the importance of Major Baltic In\ufb02ows for oxygenation of the central Baltic Sea, J. Geophys. Res. Oceans, 122, 1090\u20131101, doi:10.1002/2016JC012525.\n* Raudsepp, U., Legeais, J.-F., She, J., Maljutenko, I., Jandt, S., 2018. Baltic inflows. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, doi: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00209", "instrument": null, "keywords": "baltic-omi-wmhe-mbi-bottom-salinity-arkona-bornholm,baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-salinity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Major Baltic Inflow: bottom salinity from Reanalysis"}, "BALTIC_OMI_WMHE_mbi_sto2tz_gotland": {"abstract": "\"_DEFINITION_'\n\nMajor Baltic Inflows bring large volumes of saline and oxygen-rich water into the bottom layers of the deep basins of the central Baltic Sea, i.e. the Gotland Basin. These Major Baltic Inflows occur seldom, sometimes many years apart (Mohrholz, 2018). The Major Baltic Inflow OMI consists of the time series of the bottom layer salinity in the Arkona Basin and in the Bornholm Basin (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm) and the time-depth plot of temperature, salinity and dissolved oxygen concentration in the Gotland Basin. Temperature, salinity and dissolved oxygen profiles in the Gotland Basin enable us to estimate the amount of the Major Baltic Inflow water that has reached central Baltic, the depth interval of which has been the most affected, and how much the oxygen conditions have been improved. \n\n**CONTEXT**\n\nThe Baltic Sea is a huge brackish water basin in Northern Europe whose salinity is controlled by its freshwater budget and by the water exchange with the North Sea (e.g. Neumann et al., 2017). This implies that fresher water lies on top of water with higher salinity. The saline water inflows to the Baltic Sea through the Danish Straits, especially the Major Baltic Inflows, shape hydrophysical conditions in the Gotland Basin of the central Baltic Sea, which in turn have a substantial influence on marine ecology on different trophic levels (Bergen et al., 2018; Raudsepp et al.,2019). In the absence of the Major Baltic Inflows, oxygen in the deeper layers of the Gotland Basin is depleted and replaced by hydrogen sulphide (e.g., Savchuk, 2018). As the Baltic Sea is connected to the North Sea only through very narrow and shallow channels in the Danish Straits, inflows of high salinity and oxygenated water into the Baltic occur only intermittently (e.g., Mohrholz, 2018). Long-lasting periods of oxygen depletion in the deep layers of the central Baltic Sea accompanied by a salinity decline and overall weakening of the vertical stratification are referred to as stagnation periods. Extensive stagnation periods occurred in the 1920s/1930s, in the 1950s/1960s and in the 1980s/beginning of 1990s (Lehmann et al., 2022).\n\n**KEY FINDINGS**\n\nThe Major Baltic Inflows of 1993, 2002, and 2014 (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm) present a distinct signal in the Gotland Basin, influencing water salinity, temperature, and dissolved oxygen up to a depth of 100 meters. Following each event, deep layer salinity in the Gotland Basin increases, reaching peak bottom salinities approximately 1.5 years later, with elevated salinity levels persisting for about three years. Post-2017, salinity below 150 meters has declined, while the halocline has risen, suggesting saline water movement to the Gotland Basin's intermediate layers. Typically, temperatures fall immediately after a Major Baltic Inflow, indicating the descent of cold water from nearby upstream regions to the Gotland Deep's bottom. From 1993 to 1997, deep water temperatures remained relatively low (below 6 \u00b0C). Since 1998, these waters have warmed, with even moderate inflows in 1997/98, 2006/07, and 2018/19 introducing warmer water to the Gotland Basin's bottom layer. From 2019 onwards, water warmer than 7 \u00b0C has filled the layer beneath 100 meters depth. The water temperature below the halocline has risen by approximately 2 \u00b0C since 1993, and the cold intermediate layer's temperature has also increased from 1993 to 2023. Oxygen levels begin to drop sharply after the temporary reoxygenation of the bottom waters. The decline in 2014 was attributed to a shortage of smaller inflows that could bring oxygen-rich water to the Gotland Basin (Neumann et al., 2017) and an increase in biological oxygen demand (Savchuk, 2018; Meier et al., 2018). Additionally, warmer water has accelerated oxygen consumption in the deep layer, leading to increased anoxia. By 2023, oxygen was completely depleted below the depth of 75 metres.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00210\n\n**References:**\n\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Bergen, B., Naumann, M., Herlemann, D.P.R., Gr\u00e4we, U., Labrenz, M., J\u00fcrgens, K., 2018. Impact of a Major inflow event on the composition and distribution of bacterioplankton communities in the Baltic Sea. Frontiers in Marine Science, 5:383, doi: 10.3389/fmars.2018.00383\n* Meier, H.E.M., V\u00e4li, G., Naumann, M., Eilola, K., Frauen, C., 2018. Recently Accelerated Oxygen Consumption Rates Amplify Deoxygenation in the Baltic Sea. , J. Geophys. Res. Oceans, doi:10.1029/2017JC013686|\n* Mohrholz, V., 2018. Major Baltic Inflow Statistics \u2013 Revised. Frontiers in Marine Science, 5:384, DOI: 10.3389/fmars.2018.00384\n* Neumann, T., Radtke, H., Seifert, T., 2017. On the importance of Major Baltic In\ufb02ows for oxygenation of the central Baltic Sea, J. Geophys. Res. Oceans, 122, 1090\u20131101, doi:10.1002/2016JC012525.\n* Raudsepp, U., Maljutenko, I., K\u00f5uts, M., 2019. Cod reproductive volume potential in the Baltic Sea. In: Copernicus Marine Service Ocean State Report, Issue 3\n* Savchuk, P. 2018. Large-Scale Nutrient Dynamics in the Baltic Sea, 1970\u20132016. Frontiers in Marine Science, 5:95, doi: 10.3389/fmars.2018.00095\n", "doi": "10.48670/moi-00210", "instrument": null, "keywords": "baltic-omi-wmhe-mbi-sto2tz-gotland,baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,volume-fraction-of-oxygen-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Major Baltic Inflow: time/depth evolution S,T,O2 from Observations Reprocessing"}, "BLKSEA_ANALYSISFORECAST_BGC_007_010": {"abstract": "BLKSEA_ANALYSISFORECAST_BGC_007_010 is the nominal product of the Black Sea Biogeochemistry NRT system and is generated by the NEMO 4.2-BAMHBI modelling system. Biogeochemical Model for Hypoxic and Benthic Influenced areas (BAMHBI) is an innovative biogeochemical model with a 28-variable pelagic component (including the carbonate system) and a 6-variable benthic component ; it explicitely represents processes in the anoxic layer.\nThe product provides analysis and forecast for 3D concentration of chlorophyll, nutrients (nitrate and phosphate), dissolved oxygen, zooplankton and phytoplankton carbon biomass, oxygen-demand-units, net primary production, pH, dissolved inorganic carbon, total alkalinity, and for 2D fields of bottom oxygen concentration (for the North-Western shelf), surface partial pressure of CO2 and surface flux of CO2. These variables are computed on a grid with ~3km x 59-levels resolution, and are provided as daily and monthly means.\n\n**DOI (product):** \nhttps://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010\n\n**References:**\n\n* Gr\u00e9goire, M., Vandenbulcke, L. and Capet, A. (2020) \u201cBlack Sea Biogeochemical Analysis and Forecast (CMEMS Near-Real Time BLACKSEA Biogeochemistry).\u201d Copernicus Monitoring Environment Marine Service (CMEMS). doi: 10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010\"\n", "doi": "10.25423/CMCC/BLKSEA_ANALYSISFORECAST_BGC_007_010", "instrument": null, "keywords": "black-sea,blksea-analysisforecast-bgc-007-010,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "BLKSEA_ANALYSISFORECAST_PHY_007_001": {"abstract": "The BLKSEA_ANALYSISFORECAST_PHY_007_001 is produced with a hydrodynamic model implemented over the whole Black Sea basin, including the Azov Sea, the Bosporus Strait and a portion of the Marmara Sea for the optimal interface with the Mediterranean Sea through lateral open boundary conditions. The model horizontal grid resolution is 1/40\u00b0 in zonal and 1/40\u00b0 in meridional direction (ca. 3 km) and has 121 unevenly spaced vertical levels. The product provides analysis and forecast for 3D potential temperature, salinity, horizontal and vertical currents. Together with the 2D variables sea surface height, bottom potential temperature and mixed layer thickness.\n\n**DOI (Product)**: \nhttps://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS6\n\n**References:**\n\n* Jansen, E., Martins, D., Stefanizzi, L., Ciliberti, S. A., Gunduz, M., Ilicak, M., Lecci, R., Cret\u00ed, S., Causio, S., Aydo\u011fdu, A., Lima, L., Palermo, F., Peneva, E. L., Coppini, G., Masina, S., Pinardi, N., Palazov, A., and Valchev, N. (2022). Black Sea Physical Analysis and Forecast (Copernicus Marine Service BS-Currents, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS5\n", "doi": "10.25423/CMCC/BLKSEA_ANALYSISFORECAST_PHY_007_001_EAS6", "instrument": null, "keywords": "black-sea,blksea-analysisforecast-phy-007-001,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Physics Analysis and Forecast"}, "BLKSEA_ANALYSISFORECAST_WAV_007_003": {"abstract": "The wave analysis and forecasts for the Black Sea are produced with the third generation spectral wave model WAM Cycle 6. The hindcast and ten days forecast are produced twice a day on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 81,531. The model takes into account depth refraction, wave breaking, and assimilation of satellite wave and wind data. The system provides a hindcast and ten days forecast with one-hourly output twice a day. The atmospheric forcing is taken from ECMWF analyses and forecast data. Additionally, WAM is forced by surface currents and sea surface height from BLKSEA_ANALYSISFORECAST_PHY_007_001. Monthly statistics are provided operationally on the Product Quality Dashboard following the CMEMS metrics definitions.\n\n**Citation**: \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_WAV_007_003_EAS5\n\n**DOI (Product)**: \nhttps://doi.org/10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5\n\n**References:**\n\n* Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_ANALYSISFORECAST_WAV_007_003_EAS5\n* Ricker, M., Behrens, A., & Staneva, J. (2024). The operational CMEMS wind wave forecasting system of the Black Sea. Journal of Operational Oceanography, 1\u201322. https://doi.org/10.1080/1755876X.2024.2364974\n", "doi": "10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5", "instrument": null, "keywords": "black-sea,blksea-analysisforecast-wav-007-003,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": "proprietary", "missionStartDate": "2021-04-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Waves Analysis and Forecast"}, "BLKSEA_MULTIYEAR_BGC_007_005": {"abstract": "The biogeochemical reanalysis for the Black Sea is produced by the MAST/ULiege Production Unit by means of the BAMHBI biogeochemical model. The workflow runs on the CECI hpc infrastructure (Wallonia, Belgium).\n\n**DOI (product)**:\nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI\n\n**References:**\n\n* Gr\u00e9goire, M., Vandenbulcke, L., & Capet, A. (2020). Black Sea Biogeochemical Reanalysis (CMEMS BS-Biogeochemistry) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_REANALYSIS_BIO_007_005_BAMHBI\n", "doi": "10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI", "instrument": null, "keywords": "black-sea,blksea-multiyear-bgc-007-005,cell-thickness,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Biogeochemistry Reanalysis"}, "BLKSEA_MULTIYEAR_PHY_007_004": {"abstract": "The BLKSEA_MULTIYEAR_PHY_007_004 product provides ocean fields for the Black Sea basin starting from 01/01/1993. The hydrodynamic core is based on the NEMOv4.0 general circulation ocean model, implemented in the BS domain with horizontal resolution of 1/40\u00ba and 121 vertical levels. NEMO is forced by atmospheric fluxes computed from a bulk formulation applied to ECMWF ERA5 atmospheric fields at the resolution of 1/4\u00ba in space and 1-h in time. A heat flux correction through sea surface temperature (SST) relaxation is employed using the ESA-CCI SST-L4 product. This version has an open lateral boundary, a new model characteristic that allows a better inflow/outflow representation across the Bosphorus Strait. The model is online coupled to OceanVar assimilation scheme to assimilate sea level anomaly (SLA) along-track observations from Copernicus and available in situ vertical profiles of temperature and salinity from both SeaDataNet and Copernicus datasets. Upgrades on data assimilation include an improved background error covariance matrix and an observation-based mean dynamic topography for the SLA assimilation.\n\n**DOI (Product)**: \nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004\n\n**References:**\n\n* Lima, L., Aydogdu, A., Escudier, R., Masina, S., Ciliberti, S. A., Azevedo, D., Peneva, E. L., Causio, S., Cipollone, A., Clementi, E., Cret\u00ed, S., Stefanizzi, L., Lecci, R., Palermo, F., Coppini, G., Pinardi, N., & Palazov, A. (2020). Black Sea Physical Reanalysis (CMEMS BS-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004\n", "doi": "10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004", "instrument": null, "keywords": "black-sea,blksea-multiyear-phy-007-004,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Physics Reanalysis"}, "BLKSEA_MULTIYEAR_WAV_007_006": {"abstract": "The wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74,518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data. In addition, the product comprises a monthly climatology dataset based on significant wave height and Tm02 wave period as well as an air-sea-flux dataset.\n\n**Citation**: \nStaneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). \n\n**DOI (Product)**: \nhttps://doi.org/10.25423/cmcc/blksea_multiyear_wav_007_006_eas4\n\n**References:**\n\n* Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_WAV_007_006_EAS4\n", "doi": "10.25423/cmcc/blksea_multiyear_wav_007_006_eas4", "instrument": null, "keywords": "black-sea,blksea-multiyear-wav-007-006,charnock-coefficient-for-surface-roughness-length-for-momentum-in-air,coastal-marine-environment,eastward-friction-velocity-at-sea-water-surface,eastward-wave-mixing-momentum-flux-into-sea-water,level-4,marine-resources,marine-safety,multi-year,northward-friction-velocity-at-sea-water-surface,northward-wave-mixing-momentum-flux-into-sea-water,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-roughness-length,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting,wind-from-direction,wind-speed", "license": "proprietary", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Waves Reanalysis"}, "BLKSEA_OMI_HEALTH_oxygen_trend": {"abstract": "**DEFINITION**\n\nThe oxygenation status of the Black Sea open basin is described by three complementary indicators, derived from vertical profiles and spatially averaged over the Black Sea open basin (depth > 50m). (1) The oxygen penetration depth is the depth at which [O2] < 20\u00b5M, expressed in [m]. (2) The oxygen penetration density is the potential density anomaly at the oxygen penetration depth [kg/m\u00b3]. (3) The oxygen inventory is the vertically integrated oxygen content [mol O2/m\u00b2]. The 20\u00b5M threshold was chosen to minimize the indicator sensitivity to sensor\u2019s precision. Those three metrics are complementary: Oxygen penetration depth is more easily understood, but present more spatial variability. Oxygen penetration density helps in dissociating biogeochemical processes from shifts in the physical structure. Although less intuitive, the oxygen inventory is a more integrative diagnostic and its definition is more easily transposed to other areas.\n\n**CONTEXT**\n\nThe Black Sea is permanently stratified, due to the contrast in density between large riverine and Mediterranean inflows. This stratification restrains the ventilation of intermediate and deep waters and confines, within a restricted surface layer, the waters that are oxygenated by photosynthesis and exchanges with the atmosphere. The vertical extent of the oxic layer determines the volume of habitat available for pelagic populations (Ostrovskii and Zatsepin 2011, Sak\u0131nan and G\u00fcc\u00fc 2017) and present spatial and temporal variations (Murray et al. 1989; Tugrul et al. 1992; Konovalov and Murray 2001). At long and mid-term, these variations can be monitored with three metrics (Capet et al. 2016), derived from the vertical profiles that can obtained from traditional ship casts or autonomous Argo profilers (Stanev et al., 2013). A large source of uncertainty associated with the spatial and temporal average of those metrics stems from the small number of Argo floats, scarcely adequate to sample the known spatial variability of those metrics.\n\n**CMEMS KEY FINDINGS**\n\nDuring the past 60 years, the vertical extent of the Black Sea oxygenated layer has narrowed from 140m to 90m (Capet et al. 2016). The Argo profilers active for 2016 suggested an ongoing deoxygenation trend and indicated an average oxygen penetration depth of 72m at the end of 2016, the lowest value recorded during the past 60 years. The oxygenation of subsurface water is closely related to the intensity of cold water formation, an annual ventilation processes which has been recently limited by warmer-than-usual winter air temperature (Capet et al. 2020). In 2017, 2018 and 2020, cold waters formation resulted in a partial reoxygenation of the intermediate layer. Yet, such ventilation has been lacking in winter 2020-2021, and the updated 2021 indicators reveals the lowest oxygen inventory ever reported in this OMI time series. This results in significant detrimental trends now depicted also over the Argo period (2012-2021).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00213\n\n**References:**\n\n* Capet, A., Vandenbulcke, L., & Gr\u00e9goire, M. (2020). A new intermittent regime of convective ventilation threatens the Black Sea oxygenation status. Biogeosciences , 17(24), 6507\u20136525.\n* Capet A, Stanev E, Beckers JM, Murray J, Gr\u00e9goire M. (2016). Decline of the Black Sea oxygen inventory. Biogeosciences. 13:1287-1297.\n* Capet Arthur, Vandenbulcke Luc, Veselka Marinova, Gr\u00e9goire Marilaure. (2018). Decline of the Black Sea oxygen inventory. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Konovalov S, Murray JW. (2001). Variations in the chemistry of the Black Sea on a time scale of decades (1960\u20131995). J Marine Syst. 31: 217\u2013243.\n* Murray J, Jannasch H, Honjo S, Anderson R, Reeburgh W, Top Z, Friederich G, Codispoti L, Izdar E. (1989). Unexpected changes in the oxic/anoxic interface in the Black Sea. Nature. 338: 411\u2013413.\n* Ostrovskii A and Zatsepin A. (2011). Short-term hydrophysical and biological variability over the northeastern Black Sea continental slope as inferred from multiparametric tethered profiler surveys, Ocean Dynam., 61, 797\u2013806, 2011.\n* \u00d6zsoy E and \u00dcnl\u00fcata \u00dc. (1997). Oceanography of the Black Sea: a review of some recent results. Earth-Science Reviews. 42(4):231-72.\n* Sak\u0131nan S, G\u00fcc\u00fc AC. (2017). Spatial distribution of the Black Sea copepod, Calanus euxinus, estimated using multi-frequency acoustic backscatter. ICES J Mar Sci. 74(3):832-846. doi:10.1093/icesjms/fsw183\n* Stanev E, He Y, Grayek S, Boetius A. (2013). Oxygen dynamics in the Black Sea as seen by Argo profiling floats. Geophys Res Lett. 40(12), 3085-3090.\n* Tugrul S, Basturk O, Saydam C, Yilmaz A. (1992). Changes in the hydrochemistry of the Black Sea inferred from water density profiles. Nature. 359: 137-139.\n* von Schuckmann, K. et al. Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography 11, S1\u2013S142 (2018).\n", "doi": "10.48670/moi-00213", "instrument": null, "keywords": "black-sea,blksea-omi-health-oxygen-trend,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,ocean-mole-content-of-dissolved-molecular-oxygen,oceanographic-geographical-features,sea-water-sigma-theta-defined-by-mole-concentration-of-dissolved-molecular-oxygen-in-sea-water-above-threshold,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1955-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Oxygen Trend from Observations Reprocessing"}, "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS BLKSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_WAV_007_006) and the Analysis product (BLKSEA_ANALYSISFORECAST_WAV_007_003).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1979-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, including biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2015, IPCC, 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions (IPCC, 2021).\nSignificant Wave Height mean 99th percentile in the Black Sea region shows west-eastern dependence demonstrating that the highest values of the average annual 99th percentiles are in the areas where high winds and long fetch are simultaneously present. The largest values of the mean 99th percentile in the Black Sea in the southewestern Black Sea are around 3.5 m, while in the eastern part of the basin are around 2.5 m (Staneva et al., 2019a and 2019b).\n\n**CMEMS KEY FINDINGS**\n\nSignificant Wave Height mean 99th percentile in the Black Sea region shows west-eastern dependence with largest values in the southwestern Black Sea, with values as high as 3.5 m, while the 99th percentile values in the eastern part of the basin are around 2.5 m. The Black Sea, the 99th mean percentile for 2002-2019 shows a similar pattern demonstrating that the highest values of the mean annual 99th percentile are in the western Black Sea. This pattern is consistent with the previous studies, e.g. of (Akp\u0131nar and K\u00f6m\u00fcrc\u00fc, 2012; and Akpinar et al., 2016).\nThe anomaly of the 99th percentile in 2020 is mostly negative with values down to ~-45 cm. The highest negative anomalies for 2020 are observed in the southeastern area where the multi-year mean 99th percentile is the lowest. The highest positive anomalies of the 99th percentile in 2020 are located in the southwestern Black Sea and along the eastern coast. The map of anomalies for 2020, presenting alternate bands of positive and negative values depending on latitude, is consistent with the yearly west-east displacement of the tracks of the largest storms. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00214\n\n**References:**\n\n* Akp\u0131nar, A.; K\u00f6m\u00fcrc\u00fc, M.\u02d9I. Wave energy potential along the south-east coasts of the Black Sea. Energy 2012, 42, 289\u2013302.\n* Akp\u0131nar, A., Bing\u00f6lbali, B., Van Vledder, G., 2016. Wind and wave characteristics in the Black Sea based on the SWAN wave model forced with the CFSR winds. Ocean Eng. 126, 276\u2014298, http://dx. doi.org/10.1016/j.oceaneng.2016.09.026.\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2015. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2015 In book: Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Staneva, J. Behrens, A., Gayer G, Ricker M. (2019a) Black sea CMEMS MYP QUID Report\n* Staneva J, Behrens A., Gayer G, Aouf A., (2019b). Synergy between CMEMS products and newly available data from SENTINEL, Section 3.3, In: Schuckmann K,et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, doi: 10.1080/1755876X.2019.1633075.\n", "doi": "10.48670/moi-00214", "instrument": null, "keywords": "black-sea,blksea-omi-seastate-extreme-var-swh-mean-and-anomaly,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Significant Wave Height extreme from Reanalysis"}, "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS BLKSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_PHY_007_004) and the Analysis product (BLKSEA_ANALYSIS_FORECAST_PHYS_007_001).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. Particularly in the Black Sea, ocean-atmospheric processes together with its general cyclonic circulation (Rim Current) play an important role on the sea surface temperature variability (Capet et al. 2012). As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. The 99th mean percentile of sea surface temperature provides a worth information about the variability of the sea surface temperature and warming trends but has not been investigated with details in the Black Sea.\nWhile the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013). Recent studies indicated a warming trend of the sea surface temperature in the Black Sea in the latest years (Mulet et al., 2018; Sakali and Ba\u015fusta, 2018). A specific analysis on the interannual variability of the basin-averaged sea surface temperature revealed a higher positive trend in its eastern region (Ginzburg et al., 2004). For the past three decades, Sakali and Ba\u015fusta (2018) presented an increase in sea surface temperature that varied along both east\u2013west and south\u2013north directions in the Black Sea. \n\n**CMEMS KEY FINDINGS**\n\nThe mean annual 99th percentile in the period 1993\u20132019 exhibits values ranging from 25.50 to 26.50 oC in the western and central regions of the Black Sea. The values increase towards the east, exceeding 27.5 oC. This contrasting west-east pattern may be linked to the basin wide cyclonic circulation. There are regions showing lower values, below 25.75 oC, such as a small area west of Crimean Peninsula in the vicinity of the Sevastopol anticyclone, the Northern Ukraine region, in particular close to the Odessa and the Karkinytska Gulf due to the freshwaters from the land and a narrow area along the Turkish coastline in the south. Results for 2020 show negative anomalies in the area of influence of the Bosporus and the Bulgarian offshore region up to the Crimean peninsula, while the North West shelf exhibits a positive anomaly as in the Eastern basin. The highest positive value is occurring in the Eastern Tukish coastline nearest the Batumi gyre area. This may be related to the variously increase of sea surface temperature in such a way the southern regions have experienced a higher warming.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00216\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Capet, A., Barth, A., Beckers, J. M., & Marilaure, G. (2012). Interannual variability of Black Sea's hydrodynamics and connection to atmospheric patterns. Deep Sea Research Part II: Topical Studies in Oceanography, 77, 128-142. https://doi.org/10.1016/j.dsr2.2012.04.010\n* Ginzburg, A. I.; Kostianoy, A. G.; Sheremet, N. A. (2004). Seasonal and interannual variability of the Black Sea surface temperature as revealed from satellite data (1982\u20132000), Journal of Marine Systems, 52, 33-50. https://doi.org/10.1016/j.jmarsys.2004.05.002.\n* Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Br\u00f6nnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2013. Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 1.1, s5\u2013s13, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sakalli A, Ba\u015fusta N. 2018. Sea surface temperature change in the Black Sea under climate change: A simulation of the sea surface temperature up to 2100. International Journal of Climatology, 38(13), 4687-4698. https://doi.org/10.1002/joc.5688\n", "doi": "10.48670/moi-00216", "instrument": null, "keywords": "black-sea,blksea-omi-tempsal-extreme-var-temp-mean-and-anomaly,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Surface Temperature extreme from Reanalysis"}, "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"abstract": "\"_DEFINITION_'\n\nThe blksea_omi_tempsal_sst_area_averaged_anomalies product for 2023 includes unfiltered Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the Black Sea, and 24-month filtered SST anomalies, obtained by using the X11-seasonal adjustment procedure. This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the evolution of SST anomalies (unfiltered and filtered) over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Anomalies are computed against the 1991-2020 reference period. The 30-year climatology 1991-2020 is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018).\n\n**KEY FINDINGS**\n\nDuring 2023, the Black Sea basin average SST anomaly was ~1.1 \u00b0C above the 1991-2020 climatology, doubling that of previous year (~0.5 \u00b0C). The Black Sea SST monthly anomalies ranged between -1.0/+1.0 \u00b0C. The highest temperature anomaly (~1.8 \u00b0C) was reached in January 2023, while the lowest (~-0.28 \u00b0C) in May. This year, along with 2022, was characterized by milder temperature anomalies with respect to the previous three consecutive years (2018-2020) marked by peaks of ~3 \u00b0C occurred in May 2018, June 2019, and October 2020.\nOver the period 1982-2023, the Black Sea SST has warmed at a rate of 0.065 \u00b1 0.002 \u00b0C/year, which corresponds to an average increase of about 2.7 \u00b0C during these last 42 years.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00217\n\n**References:**\n\n* Buongiorno Nardelli, B., Colella, S. Santoleri, R., Guarracino, M., Kholod, A., 2010. A re-analysis of Black Sea surface temperature. Journal of Marine Systems, 79, Issues 1\u20132, 50-64, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2009.07.001.\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n", "doi": "10.48670/moi-00217", "instrument": null, "keywords": "black-sea,blksea-omi-tempsal-sst-area-averaged-anomalies,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Surface Temperature time series and trend from Observations Reprocessing"}, "BLKSEA_OMI_TEMPSAL_sst_trend": {"abstract": "**DEFINITION**\n\nThe blksea_omi_tempsal_sst_trend product includes the cumulative/net Sea Surface Temperature (SST) trend for the Black Sea over the period 1982-2023, i.e. the rate of change (\u00b0C/year) multiplied by the number years in the timeseries (42). This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the SST trend over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018).\n**KEY FINDINGS**\n\nOver the past four decades (1982-2023), the Black Sea surface temperature (SST) warmed at a rate of 0.065 \u00b1 0.002 \u00b0C per year, corresponding to a mean surface temperature warming of about 2.7 \u00b0C. The spatial pattern of the Black Sea SST trend reveals a general warming tendency, ranging from 0.053 \u00b0C/year to 0.080 \u00b0C/year. The spatial pattern of SST trend is rather homogeneous over the whole basin. Highest values characterize the eastern basin, where the trend reaches the extreme value, while lower values are found close to the western coasts, in correspondence of main rivers inflow. The Black Sea SST trend continues to show the highest intensity among all the other European Seas.\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00218\n\n**References:**\n\n* Buongiorno Nardelli, B., Colella, S. Santoleri, R., Guarracino, M., Kholod, A., 2010. A re-analysis of Black Sea surface temperature. Journal of Marine Systems, 79, Issues 1\u20132, 50-64, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2009.07.001.\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00218", "instrument": null, "keywords": "baltic-sea,blksea-omi-tempsal-sst-trend,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "GLOBAL_ANALYSISFORECAST_BGC_001_028": {"abstract": "The Operational Mercator Ocean biogeochemical global ocean analysis and forecast system at 1/4 degree is providing 10 days of 3D global ocean forecasts updated weekly. The time series is aggregated in time, in order to reach a two full year\u2019s time series sliding window. This product includes daily and monthly mean files of biogeochemical parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production, phytoplankton, zooplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5700 meters.\n\n* NEMO version (v3.6_STABLE)\n* Forcings: GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 at daily frequency. \n* Outputs mean fields are interpolated on a standard regular grid in NetCDF format.\n* Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related [QuID](https://documentation.marine.copernicus.eu/QUID/CMEMS-GLO-QUID-001-028.pdf)\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00015", "doi": "10.48670/moi-00015", "instrument": null, "keywords": "cell-height,cell-thickness,cell-width,coastal-marine-environment,forecast,global-analysisforecast-bgc-001-028,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "GLOBAL_ANALYSISFORECAST_PHY_001_024": {"abstract": "The Operational Mercator global ocean analysis and forecast system at 1/12 degree is providing 10 days of 3D global ocean forecasts updated daily. The time series is aggregated in time in order to reach a two full year\u2019s time series sliding window.\n\nThis product includes daily and monthly mean files of temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom over the global ocean. It also includes hourly mean surface fields for sea level height, temperature and currents. The global ocean output files are displayed with a 1/12 degree horizontal resolution with regular longitude/latitude equirectangular projection.\n\n50 vertical levels are ranging from 0 to 5500 meters.\n\nThis product also delivers a special dataset for surface current which also includes wave and tidal drift called SMOC (Surface merged Ocean Current).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00016", "doi": "10.48670/moi-00016", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,change-in-sea-floor-height-above-reference-ellipsoid-due-to-ocean-tide-loading,change-in-sea-surface-height-due-to-change-in-air-pressure,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,forecast,global-analysisforecast-phy-001-024,global-average-sea-level-change-due-to-change-in-ocean-mass,global-average-steric-sea-level-change,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-dynamic-sea-level,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-temperature-anomaly,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,tidal-sea-surface-height-above-mean-sea-level,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Physics Analysis and Forecast"}, "GLOBAL_ANALYSISFORECAST_WAV_001_027": {"abstract": "The operational global ocean analysis and forecast system of M\u00e9t\u00e9o-France with a resolution of 1/12 degree is providing daily analyses and 10 days forecasts for the global ocean sea surface waves. This product includes 3-hourly instantaneous fields of integrated wave parameters from the total spectrum (significant height, period, direction, Stokes drift,...etc), as well as the following partitions: the wind wave, the primary and secondary swell waves.\n \nThe global wave system of M\u00e9t\u00e9o-France is based on the wave model MFWAM which is a third generation wave model. MFWAM uses the computing code ECWAM-IFS-38R2 with a dissipation terms developed by Ardhuin et al. (2010). The model MFWAM was upgraded on november 2014 thanks to improvements obtained from the european research project \u00ab my wave \u00bb (Janssen et al. 2014). The model mean bathymetry is generated by using 2-minute gridded global topography data ETOPO2/NOAA. Native model grid is irregular with decreasing distance in the latitudinal direction close to the poles. At the equator the distance in the latitudinal direction is more or less fixed with grid size 1/10\u00b0. The operational model MFWAM is driven by 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF atmospheric system. The wave spectrum is discretized in 24 directions and 30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM uses the assimilation of altimeters with a time step of 6 hours. The global wave system provides analysis 4 times a day, and a forecast of 10 days at 0:00 UTC. The wave model MFWAM uses the partitioning to split the swell spectrum in primary and secondary swells.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00017\n\n**References:**\n\n* F. Ardhuin, R. Magne, J-F. Filipot, A. Van der Westhyusen, A. Roland, P. Quefeulou, J. M. Lef\u00e8vre, L. Aouf, A. Babanin and F. Collard : Semi empirical dissipation source functions for wind-wave models : Part I, definition and calibration and validation at global scales. Journal of Physical Oceanography, March 2010.\n* P. Janssen, L. Aouf, A. Behrens, G. Korres, L. Cavalieri, K. Christiensen, O. Breivik : Final report of work-package I in my wave project. December 2014.\n", "doi": "10.48670/moi-00017", "instrument": null, "keywords": "coastal-marine-environment,forecast,global-analysisforecast-wav-001-027,global-ocean,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-01-01T03:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Waves Analysis and Forecast"}, "GLOBAL_MULTIYEAR_BGC_001_029": {"abstract": "The biogeochemical hindcast for global ocean is produced at Mercator-Ocean (Toulouse. France). It provides 3D biogeochemical fields since year 1993 at 1/4 degree and on 75 vertical levels. It uses PISCES biogeochemical model (available on the NEMO modelling platform). No data assimilation in this product.\n\n* Latest NEMO version (v3.6_STABLE)\n* Forcings: FREEGLORYS2V4 ocean physics produced at Mercator-Ocean and ERA-Interim atmosphere produced at ECMWF at a daily frequency \n* Outputs: Daily (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production) and monthly (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production. iron. phytoplankton in carbon) 3D mean fields interpolated on a standard regular grid in NetCDF format. The simulation is performed once and for all.\n* Initial conditions: World Ocean Atlas 2013 for nitrate. phosphate. silicate and dissolved oxygen. GLODAPv2 for DIC and Alkalinity. and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00019", "doi": "10.48670/moi-00019", "instrument": null, "keywords": "coastal-marine-environment,global-multiyear-bgc-001-029,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,none,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Biogeochemistry Hindcast"}, "GLOBAL_MULTIYEAR_BGC_001_033": {"abstract": "The Low and Mid-Trophic Levels (LMTL) reanalysis for global ocean is produced at [CLS](https://www.cls.fr) on behalf of Global Ocean Marine Forecasting Center. It provides 2D fields of biomass content of zooplankton and six functional groups of micronekton. It uses the LMTL component of SEAPODYM dynamical population model (http://www.seapodym.eu). No data assimilation has been done. This product also contains forcing data: net primary production, euphotic depth, depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers.\n\n**Forcings sources:**\n* Ocean currents and temperature (CMEMS multiyear product)\n* Net Primary Production computed from chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high latitudes (CMEMS biogeochemistry multiyear product)\n\n**Vertical coverage:**\n* Epipelagic layer \n* Upper mesopelagic layer\n* Lower mesopelagic layer (max. 1000m)\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00020\n\n**References:**\n\n* Lehodey P., Murtugudde R., Senina I. (2010). Bridging the gap from ocean models to population dynamics of large marine predators: a model of mid-trophic functional groups. Progress in Oceanography, 84, p. 69-84.\n* Lehodey, P., Conchon, A., Senina, I., Domokos, R., Calmettes, B., Jouanno, J., Hernandez, O., Kloser, R. (2015) Optimization of a micronekton model with acoustic data. ICES Journal of Marine Science, 72(5), p. 1399-1412.\n* Conchon A. (2016). Mod\u00e9lisation du zooplancton et du micronecton marins. Th\u00e8se de Doctorat, Universit\u00e9 de La Rochelle, 136 p.\n", "doi": "10.48670/moi-00020", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity-vertical-mean-over-pelagic-layer,euphotic-zone-depth,global-multiyear-bgc-001-033,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water,northward-sea-water-velocity-vertical-mean-over-pelagic-layer,numerical-model,oceanographic-geographical-features,sea-water-pelagic-layer-bottom-depth,sea-water-potential-temperature-vertical-mean-over-pelagic-layer,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1998-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "GLOBAL_MULTIYEAR_PHY_001_030": {"abstract": "The GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12\u00b0 horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward).\n\nIt is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.\n\nThis product includes daily and monthly mean files for temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom. The global ocean output files are displayed on a standard regular grid at 1/12\u00b0 (approximatively 8 km) and on 50 standard levels.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00021", "doi": "10.48670/moi-00021", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,global-multiyear-phy-001-030,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Physics Reanalysis"}, "GLOBAL_MULTIYEAR_PHY_ENS_001_031": {"abstract": "You can find here the CMEMS Global Ocean Ensemble Reanalysis product at \u00bc degree resolution: monthly means of Temperature, Salinity, Currents and Ice variables for 75 vertical levels, starting from 1993 onward.\n \nGlobal ocean reanalyses are homogeneous 3D gridded descriptions of the physical state of the ocean covering several decades, produced with a numerical ocean model constrained with data assimilation of satellite and in situ observations. These reanalyses are built to be as close as possible to the observations (i.e. realistic) and in agreement with the model physics The multi-model ensemble approach allows uncertainties or error bars in the ocean state to be estimated.\n\nThe ensemble mean may even provide for certain regions and/or periods a more reliable estimate than any individual reanalysis product.\n\nThe four reanalyses, used to create the ensemble, covering \u201caltimetric era\u201d period (starting from 1st of January 1993) during which altimeter altimetry data observations are available:\n * GLORYS2V4 from Mercator Ocean (Fr);\n * ORAS5 from ECMWF;\n * GloSea5 from Met Office (UK);\n * and C-GLORSv7 from CMCC (It);\n \nThese four products provided four different time series of global ocean simulations 3D monthly estimates. All numerical products available for users are monthly or daily mean averages describing the ocean.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00024", "doi": "10.48670/moi-00024", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-multiyear-phy-ens-001-031,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Ensemble Physics Reanalysis"}, "GLOBAL_MULTIYEAR_WAV_001_032": {"abstract": "GLOBAL_REANALYSIS_WAV_001_032 for the global wave reanalysis describing past sea states since years 1980. This product also bears the name of WAVERYS within the GLO-HR MFC for correspondence to other global multi-year products like GLORYS. BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5\u00b0 irregular grid. Average wave quantities derived from this wave spectrum such as the SWH (significant wave height) or the average wave period are delivered on a regular 1/5\u00b0 grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from a geometric sequence of first member 0.035 Hz and a reason 7.5. WAVERYS takes into account oceanic currents from the GLORYS12 physical ocean reanalysis and assimilates SWH observed from historical altimetry missions and directional wave spectra from Sentinel 1 SAR from 2017 onwards. \n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00022", "doi": "10.48670/moi-00022", "instrument": null, "keywords": "coastal-marine-environment,global-multiyear-wav-001-032,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Waves Reanalysis"}, "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly": {"abstract": "\"_DEFINITION_'\n\nNINO34 sub surface temperature anomaly (\u00b0C) is defined as the difference between the subsurface temperature averaged over the 170\u00b0W-120\u00b0W 5\u00b0S,-5\u00b0N area and the climatological reference value over same area (GLOBAL_MULTIYEAR_PHY_ENS_001_031). Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014. \n\n**CONTEXT**\n\nEl Nino Southern Oscillation (ENSO) is one of the most important sources of climatic variability resulting from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations. Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Ni\u00f1o is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Ni\u00f1a (review in Wang et al., 2017). Nino 3.4 sub-surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific el Nino conditions and enable to monitor the evolution the ENSO phase.\n\n**CMEMS KEY FINDINGS **\n\nOver the 1993-2023 period, there were several episodes of strong positive ENSO (el nino) phases in particular during the 1997/1998 winter and the 2015/2016 winter, where NINO3.4 indicator reached positive values larger than 2\u00b0C (and remained above 0.5\u00b0C during more than 6 months). Several La Nina events were also observed like during the 1998/1999 winter and during the 2010/2011 winter. \nThe NINO34 subsurface indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of atmospheric conditions. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00220\n\n**References:**\n\n* Copernicus Marine Service Ocean State Report. (2018). Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00220", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-climvar-enso-tzt-anomaly,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nino 3.4 Temporal Evolution of Vertical Profile of Temperature from Reanalysis"}, "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nNINO34 sea surface temperature anomaly (\u00b0C) is defined as the difference between the sea surface temperature averaged over the 170\u00b0W-120\u00b0W 5\u00b0S,-5\u00b0N area and the climatological reference value over same area (GLOBAL_MULTIYEAR_PHY_ENS_001_031) . Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014. El Nino or La Nina events are defined when the NINO3.4 SST anomalies exceed +/- 0.5\u00b0C during a period of six month.\n\n**CONTEXT**\n\nEl Nino Southern Oscillation (ENSO) is one of the most important source of climatic variability resulting from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations. Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Ni\u00f1o is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Ni\u00f1a (review in Wang et al., 2017). Nino 3.4 Sea surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific El Nino conditions and enable to monitor the evolution the ENSO phase.\n\n**CMEMS KEY FINDINGS**\n\nOver the 1993-2023 period, there were several episodes of strong positive ENSO phases in particular in 1998 and 2016, where NINO3.4 indicator reached positive values larger than 2\u00b0C (and remained above 0.5\u00b0C during more than 6 months). Several La Nina events were also observed like in 2000 and 2008. \nThe NINO34 indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of meteorological conditions. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00219\n\n**References:**\n\n* Copernicus Marine Service Ocean State Report. (2018). Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00219", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-climvar-enso-sst-area-averaged-anomalies,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nino 3.4 Sea Surface Temperature time series from Reanalysis"}, "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated": {"abstract": "**DEFINITION**\n\nThe global yearly ocean CO2 sink represents the ocean uptake of CO2 from the atmosphere computed over the whole ocean. It is expressed in PgC per year. The ocean monitoring index is presented for the period 1985 to year-1. The yearly estimate of the ocean CO2 sink corresponds to the mean of a 100-member ensemble of CO2 flux estimates (Chau et al. 2022). The range of an estimate with the associated uncertainty is then defined by the empirical 68% interval computed from the ensemble.\n\n**CONTEXT**\n\nSince the onset of the industrial era in 1750, the atmospheric CO2 concentration has increased from about 277\u00b13 ppm (Joos and Spahni, 2008) to 412.44\u00b10.1 ppm in 2020 (Dlugokencky and Tans, 2020). By 2011, the ocean had absorbed approximately 28 \u00b1 5% of all anthropogenic CO2 emissions, thus providing negative feedback to global warming and climate change (Ciais et al., 2013). The ocean CO2 sink is evaluated every year as part of the Global Carbon Budget (Friedlingstein et al. 2022). The uptake of CO2 occurs primarily in response to increasing atmospheric levels. The global flux is characterized by a significant variability on interannual to decadal time scales largely in response to natural climate variability (e.g., ENSO) (Friedlingstein et al. 2022, Chau et al. 2022). \n\n**CMEMS KEY FINDINGS**\n\nThe rate of change of the integrated yearly surface downward flux has increased by 0.04\u00b10.03e-1 PgC/yr2 over the period 1985 to year-1. The yearly flux time series shows a plateau in the 90s followed by an increase since 2000 with a growth rate of 0.06\u00b10.04e-1 PgC/yr2. In 2021 (resp. 2020), the global ocean CO2 sink was 2.41\u00b10.13 (resp. 2.50\u00b10.12) PgC/yr. The average over the full period is 1.61\u00b10.10 PgC/yr with an interannual variability (temporal standard deviation) of 0.46 PgC/yr. In order to compare these fluxes to Friedlingstein et al. (2022), the estimate of preindustrial outgassing of riverine carbon of 0.61 PgC/yr, which is in between the estimate by Jacobson et al. (2007) (0.45\u00b10.18 PgC/yr) and the one by Resplandy et al. (2018) (0.78\u00b10.41 PgC/yr) needs to be added. A full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020) and in Chau et al. (2022).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00223\n\n**References:**\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Ciais, P., Sabine, C., Govindasamy, B., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Que\u0301re\u0301, C., Myneni, R., Piao, S., and Thorn- ton, P.: Chapter 6: Carbon and Other Biogeochemical Cycles, in: Climate Change 2013 The Physical Science Basis, edited by: Stocker, T., Qin, D., and Platner, G.-K., Cambridge University Press, Cambridge, 2013.\n* Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, National Oceanic and Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL), http://www.esrl. noaa.gov/gmd/ccgg/trends/global.html, last access: 11 March 2022.\n* Joos, F. and Spahni, R.: Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years, P. Natl. Acad. Sci. USA, 105, 1425\u20131430, https://doi.org/10.1073/pnas.0707386105, 2008.\n* Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Qu\u00e9r\u00e9, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., Bopp, L., Chau, T. T. T., Chevallier, F., Chini, L. P., Cronin, M., Currie, K. I., Decharme, B., Djeutchouang, L. M., Dou, X., Evans, W., Feely, R. A., Feng, L., Gasser, T., Gilfillan, D., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., G\u00fcrses, \u00d6., Harris, I., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Luijkx, I. T., Jain, A., Jones, S. D., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., K\u00f6rtzinger, A., Landsch\u00fctzer, P., Lauvset, S. K., Lef\u00e8vre, N., Lienert, S., Liu, J., Marland, G., McGuire, P. C., Melton, J. R., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y., Ono, T., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., R\u00f6denbeck, C., Rosan, T. M., Schwinger, J., Schwingshackl, C., S\u00e9f\u00e9rian, R., Sutton, A. J., Sweeney, C., Tanhua, T., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F., van der Werf, G. R., Vuichard, N., Wada, C., Wanninkhof, R., Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, C., Yue, X., Zaehle, S., and Zeng, J.: Global Carbon Budget 2021, Earth Syst. Sci. Data, 14, 1917\u20132005, https://doi.org/10.5194/essd-14-1917-2022, 2022.\n* Jacobson, A. R., Mikaloff Fletcher, S. E., Gruber, N., Sarmiento, J. L., and Gloor, M. (2007), A joint atmosphere-ocean inversion for surface fluxes of carbon dioxide: 1. Methods and global-scale fluxes, Global Biogeochem. Cycles, 21, GB1019, doi:10.1029/2005GB002556.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* Resplandy, L., Keeling, R. F., R\u00f6denbeck, C., Stephens, B. B., Khatiwala, S., Rodgers, K. B., Long, M. C., Bopp, L. and Tans, P. P.: Revision of global carbon fluxes based on a reassessment of oceanic and riverine carbon transport. Nature Geoscience, 11(7), p.504, 2018.\n", "doi": "10.48670/moi-00223", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-co2-flux-integrated,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Yearly CO2 Sink from Multi-Observations Reprocessing"}, "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged": {"abstract": "**DEFINITION**\n\nOcean acidification is quantified by decreases in pH, which is a measure of acidity: a decrease in pH value means an increase in acidity, that is, acidification. The observed decrease in ocean pH resulting from increasing concentrations of CO2 is an important indicator of global change. The estimate of global mean pH builds on a reconstruction methodology, \n* Obtain values for alkalinity based on the so called \u201clocally interpolated alkalinity regression (LIAR)\u201d method after Carter et al., 2016; 2018. \n* Build on surface ocean partial pressure of carbon dioxide (CMEMS product: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) obtained from an ensemble of Feed-Forward Neural Networks (Chau et al. 2022) which exploit sampling data gathered in the Surface Ocean CO2 Atlas (SOCAT) (https://www.socat.info/)\n* Derive a gridded field of ocean surface pH based on the van Heuven et al., (2011) CO2 system calculations using reconstructed pCO2 (MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) and alkalinity.\nThe global mean average of pH at yearly time steps is then calculated from the gridded ocean surface pH field. It is expressed in pH unit on total hydrogen ion scale. In the figure, the amplitude of the uncertainty (1\u03c3 ) of yearly mean surface sea water pH varies at a range of (0.0023, 0.0029) pH unit (see Quality Information Document for more details). The trend and uncertainty estimates amount to -0.0017\u00b10.0004e-1 pH units per year.\nThe indicator is derived from in situ observations of CO2 fugacity (SOCAT data base, www.socat.info, Bakker et al., 2016). These observations are still sparse in space and time. Monitoring pH at higher space and time resolutions, as well as in coastal regions will require a denser network of observations and preferably direct pH measurements. \nA full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020).\n\n**CONTEXT**\n\nThe decrease in surface ocean pH is a direct consequence of the uptake by the ocean of carbon dioxide. It is referred to as ocean acidification. The International Panel on Climate Change (IPCC) Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems (2011) defined Ocean Acidification as \u201ca reduction in the pH of the ocean over an extended period, typically decades or longer, which is caused primarily by uptake of carbon dioxide from the atmosphere, but can also be caused by other chemical additions or subtractions from the ocean\u201d. The pH of contemporary surface ocean waters is already 0.1 lower than at pre-industrial times and an additional decrease by 0.33 pH units is projected over the 21st century in response to the high concentration pathway RCP8.5 (Bopp et al., 2013). Ocean acidification will put marine ecosystems at risk (e.g. Orr et al., 2005; Gehlen et al., 2011; Kroeker et al., 2013). The monitoring of surface ocean pH has become a focus of many international scientific initiatives (http://goa-on.org/) and constitutes one target for SDG14 (https://sustainabledevelopment.un.org/sdg14). \n\n**CMEMS KEY FINDINGS**\n\nSince the year 1985, global ocean surface pH is decreasing at a rate of -0.0017\u00b10.0004e-1 per year. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00224\n\n**References:**\n\n* Bakker, D. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383-413, https://doi.org/10.5194/essd-8-383-2016, 2016.\n* Bopp, L. et al.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225\u20136245, doi: 10.5194/bg-10-6225-2013, 2013.\n* Carter, B.R., et al.: Updated methods for global locally interpolated estimation of alkalinity, pH, and nitrate, Limnol. Oceanogr.: Methods 16, 119\u2013131, 2018.\n* Carter, B. R., et al.: Locally interpolated alkalinity regression for global alkalinity estimation. Limnol. Oceanogr.: Methods 14: 268\u2013277. doi:10.1002/lom3.10087, 2016.\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022. Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Thi Tuyet Trang Chau, Anna Conchon, Anna Denvil-Sommer, Fr\u00e9d\u00e9ric Chevallier, Mathieu Vrac, Carlos Mejia (2020). Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097\n* IPCC, 2011: Workshop Report of the Intergovernmental Panel on Climate Change Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems. [Field, C.B., V. Barros, T.F. Stocker, D. Qin, K.J. Mach, G.-K. Plattner, M.D. Mastrandrea, M. Tignor and K.L. Ebi (eds.)]. IPCC Working Group II Technical Support Unit, Carnegie Institution, Stanford, California, United States of America, pp.164.\n* Kroeker, K. J. et al.: Meta- analysis reveals negative yet variable effects of ocean acidifica- tion on marine organisms, Ecol. Lett., 13, 1419\u20131434, 2010.\n* Orr, J. C. et al.: Anthropogenic ocean acidification over the twenty-first century and its impact on cal- cifying organisms, Nature, 437, 681\u2013686, 2005.\n* van Heuven, S., et al.: MATLAB program developed for CO2 system calculations, ORNL/CDIAC-105b, Carbon Dioxide Inf. Anal. Cent., Oak Ridge Natl. Lab., US DOE, Oak Ridge, Tenn., 2011.\n", "doi": "10.48670/moi-00224", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-ph-area-averaged,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean acidification - mean sea water pH time series and trend from Multi-Observations Reprocessing"}, "GLOBAL_OMI_HEALTH_carbon_ph_trend": {"abstract": "**DEFINITION**\n\nThis ocean monitoring indicator (OMI) consists of annual mean rates of changes in surface ocean pH (yr-1) computed at 0.25\u00b0\u00d70.25\u00b0 resolution from 1985 until the last year. This indicator is derived from monthly pH time series distributed with the Copernicus Marine product MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008 (Chau et al., 2022a). For each grid cell, a linear least-squares regression was used to fit a linear function of pH versus time, where the slope (\u03bc) and residual standard deviation (\u03c3) are defined as estimates of the long-term trend and associated uncertainty. Finally, the estimates of pH associated with the highest uncertainty, i.e., \u03c3-to-\u00b5 ratio over a threshold of 1 0%, are excluded from the global trend map (see QUID document for detailed description and method illustrations). This threshold is chosen at the 90th confidence level of all ratio values computed across the global ocean.\n\n**CONTEXT**\n\nA decrease in surface ocean pH (i.e., ocean acidification) is primarily a consequence of an increase in ocean uptake of atmospheric carbon dioxide (CO2) concentrations that have been augmented by anthropogenic emissions (Bates et al, 2014; Gattuso et al, 2015; P\u00e9rez et al, 2021). As projected in Gattuso et al (2015), \u201cunder our current rate of emissions, most marine organisms evaluated will have very high risk of impacts by 2100 and many by 2050\u201d. Ocean acidification is thus an ongoing source of concern due to its strong influence on marine ecosystems (e.g., Doney et al., 2009; Gehlen et al., 2011; P\u00f6rtner et al. 2019). Tracking changes in yearly mean values of surface ocean pH at the global scale has become an important indicator of both ocean acidification and global change (Gehlen et al., 2020; Chau et al., 2022b). In line with a sustained establishment of ocean measuring stations and thus a rapid increase in observations of ocean pH and other carbonate variables (e.g. dissolved inorganic carbon, total alkalinity, and CO2 fugacity) since the last decades (Bakker et al., 2016; Lauvset et al., 2021), recent studies including Bates et al (2014), Lauvset et al (2015), and P\u00e9rez et al (2021) put attention on analyzing secular trends of pH and their drivers from time-series stations to ocean basins. This OMI consists of the global maps of long-term pH trends and associated 1\u03c3-uncertainty derived from the Copernicus Marine data-based product of monthly surface water pH (Chau et al., 2022a) at 0.25\u00b0\u00d70.25\u00b0 grid cells over the global ocean.\n\n**CMEMS KEY FINDINGS**\n\nSince 1985, pH has been decreasing at a rate between -0.0008 yr-1 and -0.0022 yr-1 over most of the global ocean basins. Tropical and subtropical regions, the eastern equatorial Pacific excepted, show pH trends falling in the interquartile range of all the trend estimates (between -0.0012 yr-1 and -0.0018 yr-1). pH over the eastern equatorial Pacific decreases much faster, reaching a growth rate larger than -0.0024 yr-1. Such a high rate of change in pH is also observed over a sector south of the Indian Ocean. Part of the polar and subpolar North Atlantic and the Southern Ocean has no significant trend. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00277\n\n**References:**\n\n* Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O'Brien, K. M., Olsen, A., Smith, K., Cosca, C., Harasawa, S., Jones, S. D., Nakaoka, S.-I. et al.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383\u2013413, DOI:10.5194/essd-8-383- 2016, 2016.\n* Bates, N. R., Astor, Y. M., Church, M. J., Currie, K., Dore, J. E., Gonzalez-Davila, M., Lorenzoni, L., Muller-Karger, F., Olafsson, J., and Magdalena Santana-Casiano, J.: A Time-Series View of Changing Surface Ocean Chemistry Due to Ocean Uptake of Anthropogenic CO2 and Ocean Acidification, Oceanography, 27, 126\u2013141, 2014.\n* Chau, T. T. T., Gehlen, M., Chevallier, F. : Global Ocean Surface Carbon: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008, E.U. Copernicus Marine Service Information, DOI:10.48670/moi-00047, 2022a.\n* Chau, T. T. T., Gehlen, M., Chevallier, F.: Global mean seawater pH (GLOBAL_OMI_HEALTH_carbon_ph_area_averaged), E.U. Copernicus Marine Service Information, DOI: 10.48670/moi-00224, 2022b.\n* Doney, S. C., Balch, W. M., Fabry, V. J., and Feely, R. A.: Ocean Acidification: A critical emerging problem for the ocean sciences, Oceanography, 22, 16\u201325, 2009.\n* Gattuso, J-P., Alexandre Magnan, Rapha\u00ebl Bill\u00e9, William WL Cheung, Ella L. Howes, Fortunat Joos, Denis Allemand et al. \"\"Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios.\"\" Science 349, no. 6243 (2015).\n* Gehlen, M. et al.: Biogeochemical consequences of ocean acidification and feedback to the Earth system. p. 230, in: Gattuso J.-P. & Hansson L. (Eds.), Ocean acidification. Oxford: Oxford University Press., 2011.\n* Gehlen M., Chau T T T., Conchon A., Denvil-Sommer A., Chevallier F., Vrac M., Mejia C. : Ocean acidification. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI:10.1080/1755876X.2020.1785097, 2020.\n* Lauvset, S. K., Gruber, N., Landsch\u00fctzer, P., Olsen, A., and Tjiputra, J.: Trends and drivers in global surface ocean pH over the past 3 decades, Biogeosciences, 12, 1285\u20131298, DOI:10.5194/bg-12-1285-2015, 2015.\n* Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., \u00c1lvarez, M., Becker, S., Brown, P. J., Carter, B. R., Cotrim da Cunha, L., Feely, R. A., van Heuven, S., Hoppema, M., Ishii, M., Jeansson, E., Jutterstr\u00f6m, S., Jones, S. D., Karlsen, M. K., Lo Monaco, C., Michaelis, P., Murata, A., P\u00e9rez, F. F., Pfeil, B., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Velo, A., Wanninkhof, R., Woosley, R. J., and Key, R. M.: An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2021, Earth Syst. Sci. Data, 13, 5565\u20135589, DOI:10.5194/essd-13-5565-2021, 2021.\n* P\u00f6rtner, H. O. et al. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (Wiley IPCC Intergovernmental Panel on Climate Change, Geneva, 2019).\n* P\u00e9rez FF, Olafsson J, \u00d3lafsd\u00f3ttir SR, Fontela M, Takahashi T. Contrasting drivers and trends of ocean acidification in the subarctic Atlantic. Sci Rep 11, 13991, DOI:10.1038/s41598-021-93324-3, 2021.\n", "doi": "10.48670/moi-00277", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-health-carbon-ph-trend,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,trend-of-surface-ocean-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global ocean acidification - mean sea water pH trend map from Multi-Observations Reprocessing"}, "GLOBAL_OMI_NATLANTIC_amoc_26N_profile": {"abstract": "**DEFINITION**\n\nThe Atlantic Meridional Overturning profile at 26.5N is obtained by integrating the meridional transport at 26.5 N across the Atlantic basin (zonally) and then doing a cumulative integral in depth. A climatological mean is then taken over time. This is done by using GLOBAL_MULTIYEAR_PHY_ENS_001_031 over the whole time period (1993-2023) and over the period for which there are comparable observations (Apr 2004-Mar 2023). The observations come from the RAPID array (Smeed et al, 2017). \n\n**CONTEXT**\n\nThe Atlantic Meridional Overturning Circulation (AMOC) transports heat northwards in the Atlantic and plays a key role in regional and global climate (Srokosz et al, 2012). There is a northwards transport in the upper kilometer resulting from northwards flow in the Gulf Stream and wind-driven Ekman transport, and southwards flow in the ocean interior and in deep western boundary currents (Srokosz et al, 2012). There are uncertainties in the deep profile associated with how much transport is returned in the upper (1-3km) or lower (3-5km) North Atlantic deep waters (Roberts et al 2013, Sinha et al 2018).\n\n**CMEMS KEY FINDINGS** \n\nThe AMOC strength at 1000m is found to be 17.0 \u00b1 3.2 Sv (1 Sverdrup=106m3/s; range is 2 x standard deviation of multi-product). See also Jackson et al (2018).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00231\n\n**References:**\n\n* Jackson, L., C. Dubois, S. Masina, A Storto and H Zuo, 2018: Atlantic Meridional Overturning Circulation. In Copernicus Marine Service Ocean State Report, Issue 2. Journal of Operational Oceanography , 11:sup1, S65-S66, 10.1080/1755876X.2018.1489208\n* Roberts, C. D., J. Waters, K. A. Peterson, M. D. Palmer, G. D. McCarthy, E. Frajka\u2010Williams, K. Haines, D. J. Lea, M. J. Martin, D. Storkey, E. W. Blockley and H. Zuo (2013), Atmosphere drives recent interannual variability of the Atlantic meridional overturning circulation at 26.5\u00b0N, Geophys. Res. Lett., 40, 5164\u20135170 doi: 10.1002/grl.50930.\n* Sinha, B., Smeed, D.A., McCarthy, G., Moat, B.I., Josey, S.A., Hirschi, J.J.-M., Frajka-Williams, E., Blaker, A.T., Rayner, D. and Madec, G. (2018), The accuracy of estimates of the overturning circulation from basin-wide mooring arrays. Progress in Oceanography, 160. 101-123\n* Smeed D., McCarthy G., Rayner D., Moat B.I., Johns W.E., Baringer M.O. and Meinen C.S. (2017). Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2017. British Oceanographic Data Centre - Natural Environment Research Council, UK. doi: 10.5285/5acfd143-1104-7b58-e053-6c86abc0d94b\n* Srokosz, M., M. Baringer, H. Bryden, S. Cunningham, T. Delworth, S. Lozier, J. Marotzke, and R. Sutton, 2012: Past, Present, and Future Changes in the Atlantic Meridional Overturning Circulation. Bull. Amer. Meteor. Soc., 93, 1663\u20131676, https://doi.org/10.1175/BAMS-D-11-00151.1\n", "doi": "10.48670/moi-00231", "instrument": null, "keywords": "amoc-cglo,amoc-glor,amoc-glos,amoc-mean,amoc-oras,amoc-std,coastal-marine-environment,global-ocean,global-omi-natlantic-amoc-26n-profile,marine-resources,marine-safety,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Meridional Overturning Circulation AMOC profile at 26N from Reanalysis"}, "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries": {"abstract": "**DEFINITION**\n\nThe Atlantic Meridional Overturning strength at 26.5N is obtained by integrating the meridional transport at 26.5 N across the Atlantic basin (zonally) and then doing a cumulative integral in depth by using GLOBAL_MULTIYEAR_PHY_ENS_001_031 . The maximum value in depth is then taken as the strength in Sverdrups (Sv=1x106m3/s). The observations come from the RAPID array (Smeed et al, 2017).\n\n**CONTEXT**\n\nThe Atlantic Meridional Overturning Circulation (AMOC) transports heat northwards in the Atlantic and plays a key role in regional and global climate (Srokosz et al, 2012). There is a northwards transport in the upper kilometer resulting from northwards flow in the Gulf Stream and wind-driven Ekman transport, and southwards flow in the ocean interior and in deep western boundary currents (Srokosz et al, 2012). The observations have revealed variability at monthly to decadal timescales including a temporary weakening in 2009/10 (McCarthy et al, 2012) and a decrease from 2005-2012 (Smeed et al, 2014; Smeed et al, 2018). Other studies have suggested that this weakening may be a result of variability (Smeed et al, 2014; Jackson et al 2017).\n\n**CMEMS KEY FINDINGS **\n\nThe AMOC strength exhibits significant variability on many timescales with a temporary weakening in 2009/10. There has been a weakening from 2005-2012 (-0.67 Sv/year, (p=0.03) in the observations and -0.53 Sv/year (p=0.04) in the multi-product mean). The multi-product suggests an earlier increase from 2001-2006 (0.48 Sv/yr, p=0.04), and a weakening in 1998-99, however before this period there is significant uncertainty. This indicates that the changes observed are likely to be variability rather than an ongoing trend (see also Jackson et al, 2018).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00232\n\n**References:**\n\n* Jackson, L. C., Peterson, K. A., Roberts, C. D. & Wood, R. A. (2016). Recent slowing of Atlantic overturning circulation as a recovery from earlier strengthening. Nature Geosci, 9, 518\u2014522\n* Jackson, L., C. Dubois, S. Masina, A Storto and H Zuo, 2018: Atlantic Meridional Overturning Circulation. In Copernicus Marine Service Ocean State Report, Issue 2. Journal of Operational Oceanography , 11:sup1, S65-S66, 10.1080/1755876X.2018.1489208\n* McCarthy, G., Frajka-Williams, E., Johns, W. E., Baringer, M. O., Meinen, C. S., Bryden, H. L., Rayner, D., Duchez, A., Roberts, C. & Cunningham, S. A. (2012). Observed interannual variability of the Atlantic meridional overturning circulation at 26.5\u00b0N. Geophys. Res. Lett., 39, L19609+\n* Smeed, D. A., McCarthy, G. D., Cunningham, S. A., Frajka-Williams, E., Rayner, D., Johns, W. E., Meinen, C. S., Baringer, M. O., Moat, B. I., Duchez, A. & Bryden, H. L. (2014). Observed decline of the Atlantic meridional overturning circulation 2004&2012. Ocean Science, 10, 29--38.\n* Smeed D., McCarthy G., Rayner D., Moat B.I., Johns W.E., Baringer M.O. and Meinen C.S. (2017). Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2017. British Oceanographic Data Centre - Natural Environment Research Council, UK. doi: 10.5285/5acfd143-1104-7b58-e053-6c86abc0d94b\n* Smeed, D. A., Josey, S. A., Beaulieu, C., Johns, W. E., Moat, B. I., Frajka-Williams, E., Rayner, D., Meinen, C. S., Baringer, M. O., Bryden, H. L. & McCarthy, G. D. (2018). The North Atlantic Ocean Is in a State of Reduced Overturning. Geophys. Res. Lett., 45, 2017GL076350+. doi: 10.1002/2017gl076350\n* Srokosz, M., M. Baringer, H. Bryden, S. Cunningham, T. Delworth, S. Lozier, J. Marotzke, and R. Sutton, 2012: Past, Present, and Future Changes in the Atlantic Meridional Overturning Circulation. Bull. Amer. Meteor. Soc., 93, 1663\u20131676, https://doi.org/10.1175/BAMS-D-11-00151.1\n", "doi": "10.48670/moi-00232", "instrument": null, "keywords": "amoc-cglo,amoc-glor,amoc-glos,amoc-mean,amoc-oras,amoc-std,coastal-marine-environment,global-ocean,global-omi-natlantic-amoc-max26n-timeseries,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Meridional Overturning Circulation AMOC timeseries at 26N from Reanalysis"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000": {"abstract": "**DEFINITION**\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005, the upper (0-2000m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 1.0 \u00b1 0.1 W/m2. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00235\n\n**References:**\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00235", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-area-averaged-anomalies-0-2000,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_300": {"abstract": "**DEFINITION**\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005, the near-surface (0-300m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 0.4 \u00b1 0.1 W/m2. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00233\n\n**References:**\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00233", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-area-averaged-anomalies-0-300,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content (0-300m) from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_700": {"abstract": "**DEFINITION**\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005, the upper (0-700m) near-global (60\u00b0S-60\u00b0N) ocean warms at a rate of 0.6 \u00b1 0.1 W/m2. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00234\n\n**References:**\n\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00234", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-area-averaged-anomalies-0-700,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,integral-of-sea-water-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content (0-700m) from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_OHC_trend": {"abstract": "**DEFINITION**\n\nEstimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60\u00b0S-60\u00b0N aiming \ni)\tto obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.\nii)\tto monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). \nOcean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).\n\n**CMEMS KEY FINDINGS**\n\nRegional trends for the period 2005-2019 from the Copernicus Marine Service multi-ensemble approach show warming at rates ranging from the global mean average up to more than 8 W/m2 in some specific regions (e.g. northern hemisphere western boundary current regimes). There are specific regions where a negative trend is observed above noise at rates up to about -5 W/m2 such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00236\n\n**References:**\n\n* Capotondi, A., Wittenberg, A.T., Kug, J.-S., Takahashi, K. and McPhaden, M.J. (2020). ENSO Diversity. In El Ni\u00f1o Southern Oscillation in a Changing Climate (eds M.J. McPhaden, A. Santoso and W. Cai). https://doi.org/10.1002/9781119548164.ch4\n* Dubois et al., 2018 : Changes in the North Atlantic. Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s1\u2013s142, DOI: 10.1080/1755876X.2018.1489208\n* Hansen, J., Sato, M., Kharecha, P., & von Schuckmann, K. (2011). Earth\u2019s energy imbalance and implications. Atmos. Chem. Phys., 11(24), 13421\u201313449. https://doi.org/10.5194/acp-11-13421-2011\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.\n* von Schuckmann, K., A. Storto, S. Simoncelli, R. Raj, A. Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T. Szerkely, M. Mayer, D. Peterson, H. Zuo, G. Garric, M. Monier, 2018: Ocean heat content. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., Cheng, L., Palmer, M. D., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruy\u00e8res, D., Domingues, C., Garc\u00eda-Garc\u00eda, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killik, R., \u2026 Wijffels, S. E. (2020). Heat stored in the Earth system: Where does the energy go? The GCOS Earth heat inventory team. Earth Syst. Sci. Data Discuss., 2020, 1\u201345. https://doi.org/10.5194/essd-2019-255\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/moi-00236", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-ohc-trend,in-situ-observation,integral-of-sea-water-potential-temperature-wrt-depth-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Heat Content trend map from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000": {"abstract": "**DEFINITION**\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). \n\n**CONTEXT**\n\nThe global mean sea level is reflecting changes in the Earth\u2019s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005 the upper (0-2000m) near-global (60\u00b0S-60\u00b0N) thermosteric sea level rises at a rate of 1.3\u00b10.2 mm/year. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00240\n\n**References:**\n\n* Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. CifuentesJara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* WCRP Global Sea Level Group, 2018: Global sea-level budget: 1993-present. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* von Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n", "doi": "10.48670/moi-00240", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-sl-thsl-area-averaged-anomalies-0-2000,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,thermosteric-change-in-mean-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Thermosteric Sea Level anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700": {"abstract": "**DEFINITION**\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). \n\n**CONTEXT**\n\nThe global mean sea level is reflecting changes in the Earth\u2019s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019).\n\n**CMEMS KEY FINDINGS**\n\nSince the year 2005 the upper (0-700m) near-global (60\u00b0S-60\u00b0N) thermosteric sea level rises at a rate of 0.9\u00b10.1 mm/year. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00239\n\n**References:**\n\n* Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. CifuentesJara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* WCRP Global Sea Level Group, 2018: Global sea-level budget: 1993-present. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* von Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* von Schuckmann, K., & Le Traon, P.-Y. (2011). How well can we derive Global Ocean Indicators from Argo data? Ocean Sci., 7(6), 783\u2013791. https://doi.org/10.5194/os-7-783-2011\n", "doi": "10.48670/moi-00239", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-sl-thsl-area-averaged-anomalies-0-700,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,thermosteric-change-in-mean-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Thermosteric Sea Level anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_SL_thsl_trend": {"abstract": "**DEFINITION**\n\nThe temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60\u00b0S-60\u00b0N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m).\n\n**CONTEXT**\n\nMost of the interannual variability and trends in regional sea level is caused by changes in steric sea level. At mid and low latitudes, the steric sea level signal is essentially due to temperature changes, i.e. the thermosteric effect (Stammer et al., 2013, Meyssignac et al., 2016). Salinity changes play only a local role. Regional trends of thermosteric sea level can be significantly larger compared to their globally averaged versions (Storto et al., 2018). Except for shallow shelf sea and high latitudes (> 60\u00b0 latitude), regional thermosteric sea level variations are mostly related to ocean circulation changes, in particular in the tropics where the sea level variations and trends are the most intense over the last two decades. \n\n**CMEMS KEY FINDINGS**\n\nSignificant (i.e. when the signal exceeds the noise) regional trends for the period 2005-2019 from the Copernicus Marine Service multi-ensemble approach show a thermosteric sea level rise at rates ranging from the global mean average up to more than 8 mm/year. There are specific regions where a negative trend is observed above noise at rates up to about -8 mm/year such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00241\n\n**References:**\n\n* Capotondi, A., Wittenberg, A.T., Kug, J.-S., Takahashi, K. and McPhaden, M.J. (2020). ENSO Diversity. In El Ni\u00f1o Southern Oscillation in a Changing Climate (eds M.J. McPhaden, A. Santoso and W. Cai). https://doi.org/10.1002/9781119548164.ch4\n* Dubois et al., 2018 : Changes in the North Atlantic. Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s1\u2013s142, DOI: 10.1080/1755876X.2018.1489208\n* Storto et al., 2018: Thermosteric Sea Level. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Stammer D, Cazenave A, Ponte RM, Tamisiea ME (2013) Causes for contemporary regional sea level changes. Ann Rev Mar Sci 5:21\u201346. doi:10.1146/annurev-marine-121211-172406\n* Meyssignac, B., C. G. Piecuch, C. J. Merchant, M.-F. Racault, H. Palanisamy, C. MacIntosh, S. Sathyendranath, R. Brewin, 2016: Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993\u20132011\n", "doi": "10.48670/moi-00241", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-sl-thsl-trend,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Thermosteric Sea Level trend map from Reanalysis & Multi-Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_Tyz_trend": {"abstract": "**DEFINITION**\n\nThe linear change of zonal mean subsurface temperature over the period 1993-2019 at each grid point (in depth and latitude) is evaluated to obtain a global mean depth-latitude plot of subsurface temperature trend, expressed in \u00b0C.\nThe linear change is computed using the slope of the linear regression at each grid point scaled by the number of time steps (27 years, 1993-2019). A multi-product approach is used, meaning that the linear change is first computed for 5 different zonal mean temperature estimates. The average linear change is then computed, as well as the standard deviation between the five linear change computations. The evaluation method relies in the study of the consistency in between the 5 different estimates, which provides a qualitative estimate of the robustness of the indicator. See Mulet et al. (2018) for more details.\n\n**CONTEXT**\n\nLarge-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions, while the deeper ocean temperature in the main thermocline and below varies due to many dynamical forcing mechanisms (Bindoff et al., 2019). Together with ocean acidification and deoxygenation (IPCC, 2019), ocean warming can lead to dramatic changes in ecosystem assemblages, biodiversity, population extinctions, coral bleaching and infectious disease, change in behavior (including reproduction), as well as redistribution of habitat (e.g. Gattuso et al., 2015, Molinos et al., 2016, Ramirez et al., 2017). Ocean warming also intensifies tropical cyclones (Hoegh-Guldberg et al., 2018; Trenberth et al., 2018; Sun et al., 2017).\n\n**CMEMS KEY FINDINGS**\n\nThe results show an overall ocean warming of the upper global ocean over the period 1993-2019, particularly in the upper 300m depth. In some areas, this warming signal reaches down to about 800m depth such as for example in the Southern Ocean south of 40\u00b0S. In other areas, the signal-to-noise ratio in the deeper ocean layers is less than two, i.e. the different products used for the ensemble mean show weak agreement. However, interannual-to-decadal fluctuations are superposed on the warming signal, and can interfere with the warming trend. For example, in the subpolar North Atlantic decadal variations such as the so called \u2018cold event\u2019 prevail (Dubois et al., 2018; Gourrion et al., 2018), and the cumulative trend over a quarter of a decade does not exceed twice the noise level below about 100m depth.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00244\n\n**References:**\n\n* Dubois, C., K. von Schuckmann, S. Josey, A. Ceschin, 2018: Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208.\n* Gattuso, J-P., et al. (2015): Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, no. 6243.\n* Gourrion, J., J. Deshayes, M. Juza, T. Szekely, J. Tontore, 2018: A persisting regional cold and fresh water anomaly in the Northern Atlantic. In: Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208.\n* Hoegh-Guldberg, O., et al., 2018: Impacts of 1.5\u00baC Global Warming on Natural and Human Systems. In: Global Warming of 1.5\u00b0C. An IPCC Special Report on the impacts of global warming of 1.5\u00b0C above preindustrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. P\u00f6rtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma- Okia, C. P\u00e9an, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press.\n* IPCC, 2019: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Po\u0308rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegri\u0301a, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.\n* Molinos, J.G., et al. (2016): Climate velocity and the future global redistribution of marine biodiversity, NATURE Climate Change 6 doi:10.10383/NCLIMATE2769.\n* Mulet S, Buongiorno Nardelli B, Good S, A. Pisano A, E. Greiner, Monier M, 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208.\n* Ram\u00edrez, F., I. Af\u00e1n, L.S. Davis, and A. Chiaradia (2017): Climate impacts on global hot spots of marine biodiversity. Science Advances 3, no. 2 : e1601198.\n* Sun, Y., Z. Zhong, T. Li, L. Yi, Y. Hu, H. Wan, H. Chen, Q. Liao, C. Ma and Q. Li, 2017: Impact of Ocean Warming on Tropical Cyclone Size and Its Destructiveness, Nature Scientific Reports, Volume 7 (8154), https://doi.org/10.1038/s41598-017-08533-6.\n* Trenberth, K. E., L. J. Cheng, P. Jacobs, Y. X. Zhang, and J. Fasullo (2018): Hurricane Harvey links to ocean heat content and climate change adaptation. Earth's Future, 6, 730--744, https://doi.org/10.1029/2018EF000825.\n", "doi": "10.48670/moi-00244", "instrument": null, "keywords": "change-over-time-in-sea-water-temperature,coastal-marine-environment,global-ocean,global-omi-tempsal-tyz-trend,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Zonal Mean Subsurface Temperature cumulative trend from Multi-Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nBased on daily, global climate sea surface temperature (SST) analyses generated by the European Space Agency (ESA) SST Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) (Merchant et al., 2019; product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). \nAnalysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: \n1.\tThe daily analyses were averaged to create monthly means. \n2.\tA climatology was calculated by averaging the monthly means over the period 1993 - 2014. \n3.\tMonthly anomalies were calculated by differencing the monthly means and the climatology. \n4.\tAn area averaged time series was calculated by averaging the monthly fields over the globe, with each grid cell weighted according to its area. \n5.\tThe time series was passed through the X11 seasonal adjustment procedure, which decomposes the time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. \n6.\tThe slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016).\n\n**CMEMS KEY FINDINGS**\n\nOver the period 1993 to 2021, the global average linear trend was 0.015 \u00b1 0.001\u00b0C / year (95% confidence interval). 2021 is nominally the sixth warmest year in the time series. Aside from this trend, variations in the time series can be seen which are associated with changes between El Ni\u00f1o and La Ni\u00f1a conditions. For example, peaks in the time series coincide with the strong El Ni\u00f1o events that occurred in 1997/1998 and 2015/2016 (Gasparin et al., 2018).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00242\n\n**References:**\n\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Gasparin, F., von Schuckmann, K., Desportes, C., Sathyendranath, S. and Pardo, S. 2018. El Ni\u00f1o southern oscillation. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* Good, S.A., Kennedy, J.J, and Embury, O. Global sea surface temperature anomalies in 2018 and historical changes since 1993. In: von Schuckmann et al. 2020, Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, doi: 10.1080/1755876X.2020.1785097.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\u202f\n* Mulet S., Nardelli B.B., Good S., Pisano A., Greiner E., Monier M., Autret E., Axell L., Boberg F., Ciliberti S. 2018. Ocean temperature and salinity. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* Pezzulli, S., Stephenson, D.B. and Hannachi A. 2005. The variability of seasonality. J Clim. 18: 71\u2013 88, doi: 10.1175/JCLI-3256.1.\n* Roquet H , Pisano A., Embury O. 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus marine environment monitoring service ocean state report. J Oper Ocean. 9(suppl. 2). doi:10.1080/1755876X.2016.1273446.\n* Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63: 1379\u2013 1389, doi: 10.1080/01621459.1968.10480934.\n", "doi": "10.48670/moi-00242", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-tempsal-sst-area-averaged-anomalies,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Sea Surface Temperature time series and trend from Observations Reprocessing"}, "GLOBAL_OMI_TEMPSAL_sst_trend": {"abstract": "**DEFINITION**\n\nBased on daily, global climate sea surface temperature (SST) analyses generated by the European Space Agency (ESA) SST Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) (Merchant et al., 2019; product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). \nAnalysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: \n1.\tThe daily analyses were averaged to create monthly means. \n2.\tA climatology was calculated by averaging the monthly means over the period 1993 - 2014. \n3.\tMonthly anomalies were calculated by differencing the monthly means and the climatology. \n4.\tThe time series for each grid cell was passed through the X11 seasonal adjustment procedure, which decomposes a time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. \n5.\tThe slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016).\n\n**CMEMS KEY FINDINGS**\n\nWarming trends occurred over most of the globe between 1993 and 2021. One of the exceptions is the North Atlantic, which has a region south of Greenland where a cooling trend is found. The cooling in this area has been previously noted as occurring on centennial time scales (IPCC, 2013; Caesar et al., 2018; Sevellee et al., 2017).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00243\n\n**References:**\n\n* Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G. and Saba, V., 2018. Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature, 556(7700), p.191. DOI: 10.1038/s41586-018-0006-5.\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Good, S.A., Kennedy, J.J, and Embury, O. Global sea surface temperature anomalies in 2018 and historical changes since 1993. In: von Schuckmann et al. 2020, Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, doi: 10.1080/1755876X.2020.1785097.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\u202f\n* Mulet S., Nardelli B.B., Good S., Pisano A., Greiner E., Monier M., Autret E., Axell L., Boberg F., Ciliberti S. 2018. Ocean temperature and salinity. In: Copernicus marine service ocean state report, issue 2. J Operat Oceanogr. 11(Sup1):s11\u2013ss4. doi:10.1080/1755876X.2018.1489208.\n* IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.\n* Pezzulli, S., Stephenson, D.B. and Hannachi A. 2005. The variability of seasonality. J Clim. 18: 71\u2013 88, doi: 10.1175/JCLI-3256.1.\n* Roquet H , Pisano A., Embury O. 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus marine environment monitoring service ocean state report. J Oper Ocean. 9(suppl. 2). doi:10.1080/1755876X.2016.1273446.\n* Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63: 1379\u2013 1389, doi: 10.1080/01621459.1968.10480934.\n* S\u00e9vellec, F., Fedorov, A.V. and Liu, W., 2017. Arctic sea-ice decline weakens the Atlantic meridional overturning circulation. Nature Climate Change, 7(8), p.604, doi: 10.1038/nclimate3353.\n", "doi": "10.48670/moi-00243", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-tempsal-sst-trend,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Sea Surface Temperature trend map from Observations Reprocessing"}, "GLOBAL_OMI_WMHE_heattrp": {"abstract": "**DEFINITION**\n\nHeat transport across lines are obtained by integrating the heat fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models\u2019 daily output.\nThe mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW).\n\n**CONTEXT**\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\n**CMEMS KEY FINDINGS**\n\nThe mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00245\n\n**References:**\n\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n* Bricaud C, Drillet Y, Garric G. 2016. Ocean mass and heat transport. In CMEMS Ocean State Report, Journal of Operational Oceanography, 9, http://dx.doi.org/10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00245", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-wmhe-heattrp,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mean Heat Transport across sections from Reanalysis"}, "GLOBAL_OMI_WMHE_northward_mht": {"abstract": "**DEFINITION**\n\nMeridional Heat Transport is computed by integrating the heat fluxes along the zonal direction and from top to bottom of the ocean. \nThey are given over 3 basins (Global Ocean, Atlantic Ocean and Indian+Pacific Ocean) and for all the grid points in the meridional grid of each basin. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW).\n\n**CONTEXT**\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\n**CMEMS KEY FINDINGS**\n\nAfter an anusual 2016 year (Bricaud 2016), with a higher global meridional heat transport in the tropical band explained by, the increase of northward heat transport at 5-10 \u00b0 N in the Pacific Ocean during the El Ni\u00f1o event, 2017 northward heat transport is lower than the 1993-2014 reference value in the tropical band, for both Atlantic and Indian + Pacific Oceans. At the higher latitudes, 2017 northward heat transport is closed to 1993-2014 values.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00246\n\n**References:**\n\n* Crosnier L, Barnier B, Treguier AM, 2001. Aliasing inertial oscillations in a 1/6\u00b0 Atlantic circulation model: impact on the mean meridional heat transport. Ocean Modelling, vol 3, issues 1-2, pp21-31. https://doi.org/10.1016/S1463-5003(00)00015-9\n* Ganachaud, A. , Wunsch C. 2003. Large-Scale Ocean Heat and Freshwater Transports during the World Ocean Circulation Experiment. J. Climate, 16, 696\u2013705, https://doi.org/10.1175/1520-0442(2003)016<0696:LSOHAF>2.0.CO;2\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n", "doi": "10.48670/moi-00246", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-wmhe-northward-mht,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Northward Heat Transport for Global Ocean, Atlantic and Indian+Pacific basins from Reanalysis"}, "GLOBAL_OMI_WMHE_voltrp": {"abstract": "**DEFINITION**\n\nVolume transport across lines are obtained by integrating the volume fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models\u2019 daily output.\nThe mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in Sverdrup (Sv).\n\n**CONTEXT**\n\nThe ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth\u2019s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth\u2019s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. \n\n**CMEMS KEY FINDINGS**\n\nThe mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. At Drake Passage, the multi-product approach (product no. 2.4.1) is larger than the value (130 Sv) of Lumpkin and Speer (2007), but smaller than the new observational based results of Colin de Verdi\u00e8re and Ollitrault, (2016) (175 Sv) and Donohue (2017) (173.3 Sv).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00247\n\n**References:**\n\n* Lumpkin R, Speer K. 2007. Global ocean meridional overturning. J. Phys. Oceanogr., 37, 2550\u20132562, doi:10.1175/JPO3130.1.\n* Madec G : NEMO ocean engine, Note du P\u00f4le de mod\u00e9lisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008\n", "doi": "10.48670/moi-00247", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,global-omi-wmhe-voltrp,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Mercator Oc\u00e9an International", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mean Volume Transport across sections from Reanalysis"}, "IBI_ANALYSISFORECAST_BGC_005_004": {"abstract": "The IBI-MFC provides a high-resolution biogeochemical analysis and forecast product covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as daily averaged forecasts with a horizon of 10 days (updated on a weekly basis) are available on the catalogue.\nTo this aim, an online coupled physical-biogeochemical operational system is based on NEMO-PISCES at 1/36\u00b0 and adapted to the IBI area, being Mercator-Ocean in charge of the model code development. PISCES is a model of intermediate complexity, with 24 prognostic variables. It simulates marine biological productivity of the lower trophic levels and describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe).\nThe product provides daily and monthly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, surface partial pressure of carbon dioxide, zooplankton and light attenuation.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00026\n\n**References:**\n\n* Gutknecht, E. and Reffray, G. and Mignot, A. and Dabrowski, T. and Sotillo, M. G. Modelling the marine ecosystem of Iberia-Biscay-Ireland (IBI) European waters for CMEMS operational applications. Ocean Sci., 15, 1489\u20131516, 2019. https://doi.org/10.5194/os-15-1489-2019\n", "doi": "10.48670/moi-00026", "instrument": null, "keywords": "coastal-marine-environment,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,ibi-analysisforecast-bgc-005-004,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "IBI_ANALYSISFORECAST_PHY_005_001": {"abstract": "The IBI-MFC provides a high-resolution ocean analysis and forecast product (daily run by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as forecasts of different temporal resolutions with a horizon of 10 days (updated on a daily basis) are available on the catalogue.\nThe system is based on a eddy-resolving NEMO model application at 1/36\u00ba horizontal resolution, being Mercator-Ocean in charge of the model code development. The hydrodynamic forecast includes high frequency processes of paramount importance to characterize regional scale marine processes: tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge, wave forcing in forecast, etc. A weekly update of IBI downscaled analysis is also delivered as historic IBI best estimates.\nThe product offers 3D daily and monthly ocean fields, as well as hourly mean and 15-minute instantaneous values for some surface variables. Daily and monthly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are delivered. Doodson-filtered detided mean sea level and horizontal surface currents are also provided. Finally, 15-minute instantaneous values of Sea Surface Height and Currents are also given.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00027\n\n**References:**\n\n* Sotillo, M.G.; Campuzano, F.; Guihou, K.; Lorente, P.; Olmedo, E.; Matulka, A.; Santos, F.; Amo-Baladr\u00f3n, M.A.; Novellino, A. River Freshwater Contribution in Operational Ocean Models along the European Atlantic Fa\u00e7ade: Impact of a New River Discharge Forcing Data on the CMEMS IBI Regional Model Solution. J. Mar. Sci. Eng. 2021, 9, 401. https://doi.org/10.3390/jmse9040401\n* Mason, E. and Ruiz, S. and Bourdalle-Badie, R. and Reffray, G. and Garc\u00eda-Sotillo, M. and Pascual, A. New insight into 3-D mesoscale eddy properties from CMEMS operational models in the western Mediterranean. Ocean Sci., 15, 1111\u20131131, 2019. https://doi.org/10.5194/os-15-1111-2019\n* Lorente, P. and Garc\u00eda-Sotillo, M. and Amo-Baladr\u00f3n, A. and Aznar, R. and Levier, B. and S\u00e1nchez-Garrido, J. C. and Sammartino, S. and de Pascual-Collar, \u00c1. and Reffray, G. and Toledano, C. and \u00c1lvarez-Fanjul, E. Skill assessment of global, regional, and coastal circulation forecast models: evaluating the benefits of dynamical downscaling in IBI (Iberia-Biscay-Ireland) surface waters. Ocean Sci., 15, 967\u2013996, 2019. https://doi.org/10.5194/os-15-967-2019\n* Aznar, R., Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Amo-Baladr\u00f3n, A., Reffray, G., and Alvarez Fanjul, E. Strengths and weaknesses of the CMEMS forecasted and reanalyzed solutions for the Iberia-Biscay-Ireland (IBI) waters. J. Mar. Syst., 159, 1\u201314, https://doi.org/10.1016/j.jmarsys.2016.02.007, 2016\n* Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Reffray, G., Amo-Baladr\u00f3n, A., Benkiran, M., and Alvarez Fanjul, E.: The MyOcean IBI Ocean Forecast and Reanalysis Systems: operational products and roadmap to the future Copernicus Service, J. Oper. Oceanogr., 8, 63\u201379, https://doi.org/10.1080/1755876X.2015.1014663, 2015.\n", "doi": "10.48670/moi-00027", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,forecast,iberian-biscay-irish-seas,ibi-analysisforecast-phy-005-001,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "IBI_ANALYSISFORECAST_WAV_005_005": {"abstract": "The IBI-MFC provides a high-resolution wave analysis and forecast product (run twice a day by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates), as well as hourly instantaneous forecasts with a horizon of up to 10 days (updated on a daily basis) are available on the catalogue.\nThe IBI wave model system is based on the MFWAM model and runs on a grid of 1/36\u00ba of horizontal resolution forced with the ECMWF hourly wind data. The system assimilates significant wave height (SWH) altimeter data and CFOSAT wave spectral data (supplied by M\u00e9t\u00e9o-France), and it is forced by currents provided by the IBI ocean circulation system. \nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Additionally, the IBI wave system is set up to provide internally some key parameters adequate to be used as forcing in the IBI NEMO ocean model forecast run.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00025\n\n**References:**\n\n* Toledano, C.; Ghantous, M.; Lorente, P.; Dalphinet, A.; Aouf, L.; Sotillo, M.G. Impacts of an Altimetric Wave Data Assimilation Scheme and Currents-Wave Coupling in an Operational Wave System: The New Copernicus Marine IBI Wave Forecast Service. J. Mar. Sci. Eng. 2022, 10, 457. https://doi.org/10.3390/jmse10040457\n", "doi": "10.48670/moi-00025", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,forecast,iberian-biscay-irish-seas,ibi-analysisforecast-wav-005-005,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),wave-spectra,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-11-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast"}, "IBI_MULTIYEAR_BGC_005_003": {"abstract": "The IBI-MFC provides a biogeochemical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources.\nTo this aim, an application of the biogeochemical model PISCES is run simultaneously with the ocean physical IBI reanalysis, generating both products at the same 1/12\u00b0 horizontal resolution. The PISCES model is able to simulate the first levels of the marine food web, from nutrients up to mesozooplankton and it has 24 state variables.\nThe product provides daily, monthly and yearly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, zooplankton and surface partial pressure of carbon dioxide. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered.\nFor all the abovementioned variables new interim datasets will be provided to cover period till month - 4.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00028\n\n**References:**\n\n* Aznar, R., Sotillo, M. G., Cailleau, S., Lorente, P., Levier, B., Amo-Baladr\u00f3n, A., Reffray, G., and Alvarez Fanjul, E. Strengths and weaknesses of the CMEMS forecasted and reanalyzed solutions for the Iberia-Biscay-Ireland (IBI) waters. J. Mar. Syst., 159, 1\u201314, https://doi.org/10.1016/j.jmarsys.2016.02.007, 2016\n", "doi": "10.48670/moi-00028", "instrument": null, "keywords": "coastal-marine-environment,euphotic-zone-depth,iberian-biscay-irish-seas,ibi-multiyear-bgc-005-003,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-08-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "IBI_MULTIYEAR_PHY_005_002": {"abstract": "The IBI-MFC provides a ocean physical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources. \nThe IBI model numerical core is based on the NEMO v3.6 ocean general circulation model run at 1/12\u00b0 horizontal resolution. Altimeter data, in situ temperature and salinity vertical profiles and satellite sea surface temperature are assimilated.\nThe product offers 3D daily, monthly and yearly ocean fields, as well as hourly mean fields for surface variables. Daily, monthly and yearly averages of 3D Temperature, 3D Salinity, 3D Zonal, Meridional and vertical Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are distributed. Besides, daily means of air-sea fluxes are provided. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered. For all the abovementioned variables new interim datasets will be provided to cover period till month - 4.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00029", "doi": "10.48670/moi-00029", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,iberian-biscay-irish-seas,ibi-multiyear-phy-005-002,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-net-downward-longwave-flux,upward-sea-water-velocity,water-flux-out-of-sea-ice-and-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-08-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "IBI_MULTIYEAR_WAV_005_006": {"abstract": "The IBI-MFC provides a high-resolution wave reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1980 and being regularly extended on a yearly basis. The model system is run by Nologin with the support of CESGA in terms of supercomputing resources. \nThe Multi-Year model configuration is based on the MFWAM model developed by M\u00e9t\u00e9o-France (MF), covering the same region as the IBI-MFC Near Real Time (NRT) analysis and forecasting product and with the same horizontal resolution (1/36\u00ba). The system assimilates significant wave height (SWH) altimeter data and wave spectral data (Envisat and CFOSAT), supplied by MF. Both, the MY and the NRT products, are fed by ECMWF hourly winds. Specifically, the MY system is forced by the ERA5 reanalysis wind data. As boundary conditions, the NRT system uses the 2D wave spectra from the Copernicus Marine GLOBAL forecast system, whereas the MY system is nested to the GLOBAL reanalysis.\nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Besides, air-sea fluxes are provided. Additionally, climatological parameters of significant wave height (VHM0) and zero -crossing wave period (VTM02) are delivered for the time interval 1993-2016.\n\n**DOI (Product)**: \nhttps://doi.org/10.48670/moi-00030", "doi": "10.48670/moi-00030", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,iberian-biscay-irish-seas,ibi-multiyear-wav-005-006,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),surface-downward-eastward-stress-due-to-ocean-viscous-dissipation,surface-downward-northward-stress-due-to-ocean-viscous-dissipation,wave-mixing-energy-flux-into-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "IBI_OMI_CURRENTS_cui": {"abstract": "**DEFINITION**\n\nThe Coastal Upwelling Index (CUI) is computed along the African and the Iberian Peninsula coasts. For each latitudinal point from 27\u00b0N to 42\u00b0N the Coastal Upwelling Index is defined as the temperature difference between the maximum and minimum temperature in a range of distance from the coast up to 3.5\u00ba westwards.\n\u3016CUI\u3017_lat=max\u2061(T_lat )-min\u2061(T_lat)\nA high Coastal Upwelling Index indicates intense upwelling conditions.\nThe index is computed from the following Copernicus Marine products:\n\tIBI-MYP: IBI_MULTIYEAR_PHY_005_002 (1993-2019)\n\tIBI-NRT: IBI_ANALYSISFORECAST_PHYS_005_001 (2020 onwards)\n\n**CONTEXT**\n\nCoastal upwelling process occurs along coastlines as the result of deflection of the oceanic water away from the shore. Such deflection is produced by Ekman transport induced by persistent winds parallel to the coastline (Sverdrup, 1938). When this transported water is forced, the mass balance is maintained by pumping of ascending intermediate water. This water is typically denser, cooler and richer in nutrients. The Iberia-Biscay-Ireland domain contains two well-documented Eastern Boundary Upwelling Ecosystems, they are hosted under the same system known as Canary Current Upwelling System (Fraga, 1981; Hempel, 1982). This system is one of the major coastal upwelling regions of the world and it is produced by the eastern closure of the Subtropical Gyre. The North West African (NWA) coast presents an intense upwelling region that extends from Morocco to south of Senegal, likewise the western coast of the Iberian Peninsula (IP) shows a seasonal upwelling behavior. These two upwelling domains are separated by the presence of the Gulf of Cadiz, where the coastline does not allow the formation of upwelling conditions from 34\u00baN up to 37\u00baN.\nThe Copernicus Marine Service Coastal Upwelling Index is defined following the steps of several previous upwelling indices described in literature. More details and full scientific evaluation can be found in the dedicated section in the first issue of the Copernicus Marine Service Ocean State report (Sotillo et al., 2016).\n\n**CMEMS KEY FINDINGS**\n\nThe NWA coast (latitudes below 34\u00baN) shows a quite constantlow variability of the periodicity and the intensity of the upwelling, few periods of upwelling intensifications are found in years 1993-1995, and 2003-2004.\nIn the IP coast (latitudes higher than 37\u00baN) the interannual variability is more remarkable showing years with high upwelling activity (1994, 2004, and 2015-2017) followed by periods of lower activity (1996-1998, 2003, 2011, and 2013).\nAccording to the results of the IBI-NRT system, 2020 was a year with weak upwelling in the IP latitudes. \nWhile in the NWA coast the upwelling activity was more intense than the average.\nThe analysis of trends in the period 1993-2019 shows significant positive trend of the upwelling index in the IP latitudes. This trend implies an increase of temperature differences between the coastal and offshore waters of approximately 0.02 \u00b0C/Year.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00248\n\n**References:**\n\n* Fraga F. 1981. Upwelling off the Galician Coast, Northwest Spain. In: Richardson FA, editor. Coastal Upwelling. Washington (DC): Am Geoph Union; p. 176\u2013182.\n* Hempel G. 1982. The Canary current: studies of an upwelling system. Introduction. Rapp. Proc. Reun. Cons. Int. Expl. Mer., 180, 7\u20138.\n* Sotillo MG, Levier B, Pascual A, Gonzalez A. 2016 Iberian-Biscay-Irish Sea. In von Scuckmann et al. (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* Sverdrup HV. 1938. On the process of upwelling. J Mar Res. 1:155\u2013164.\n", "doi": "10.48670/moi-00248", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-currents-cui,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Coastal Upwelling Index from Reanalysis"}, "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS IBI_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_WAV_005_006) and the Analysis product (IBI_ANALYSIS_FORECAST_WAV_005_005).\nTwo parameters have been considered for this OMI:\n\u2022\tMap of the 99th mean percentile: It is obtained from the Multi-Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2021).\n\u2022\tAnomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2022.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe sea state and its related spatio-temporal variability affect dramatically maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions.\nThe Iberia-Biscay-Ireland region, which covers the North-East Atlantic Ocean from Canary Islands to Ireland, is characterized by two different sea state wave climate regions: whereas the northern half, impacted by the North Atlantic subpolar front, is of one of the world\u2019s greatest wave generating regions (M\u00f8rk et al., 2010; Folley, 2017), the southern half, located at subtropical latitudes, is by contrast influenced by persistent trade winds and thus by constant and moderate wave regimes.\nThe North Atlantic Oscillation (NAO), which refers to changes in the atmospheric sea level pressure difference between the Azores and Iceland, is a significant driver of wave climate variability in the Northern Hemisphere. The influence of North Atlantic Oscillation on waves along the Atlantic coast of Europe is particularly strong in and has a major impact on northern latitudes wintertime (Mart\u00ednez-Asensio et al. 2016; Bacon and Carter, 1991; Bouws et al., 1996; Bauer, 2001; Wolf et al., 2002; Gleeson et al., 2017). Swings in the North Atlantic Oscillation index produce changes in the storms track and subsequently in the wind speed and direction over the Atlantic that alter the wave regime. When North Atlantic Oscillation index is in its positive phase, storms usually track northeast of Europe and enhanced westerly winds induce higher than average waves in the northernmost Atlantic Ocean. Conversely, in the negative North Atlantic Oscillation phase, the track of the storms is more zonal and south than usual, with trade winds (mid latitude westerlies) being slower and producing higher than average waves in southern latitudes (Marshall et al., 2001; Wolf et al., 2002; Wolf and Woolf, 2006). \nAdditionally a variety of previous studies have uniquevocally determined the relationship between the sea state variability in the IBI region and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Mart\u00ednez-Asensio et al., 2016). \nIn this context, long\u2010term statistical analysis of reanalyzed model data is mandatory not only to disentangle other driving agents of wave climate but also to attempt inferring any potential trend in the number and/or intensity of extreme wave events in coastal areas with subsequent socio-economic and environmental consequences.\n\n**CMEMS KEY FINDINGS**\n\nThe climatic mean of 99th percentile (1993-2021) reveals a north-south gradient of Significant Wave Height with the highest values in northern latitudes (above 8m) and lowest values (2-3 m) detected southeastward of Canary Islands, in the seas between Canary Islands and the African Continental Shelf. This north-south pattern is the result of the two climatic conditions prevailing in the region and previously described.\nThe 99th percentile anomalies in 2023 show that during this period, the central latitudes of the domain (between 37 \u00baN and 50 \u00baN) were affected by extreme wave events that exceeded up to twice the standard deviation of the anomalies. These events impacted not only the open waters of the Northeastern Atlantic but also European coastal areas such as the west coast of Portugal, the Spanish Atlantic coast, and the French coast, including the English Channel.\nAdditionally, the impact of significant wave extremes exceeding twice the standard deviation of anomalies was detected in the Mediterranean region of the Balearic Sea and the Algerian Basin. This pattern is commonly associated with the impact of intense Tramontana winds originating from storms that cross the Iberian Peninsula from the Gulf of Biscay.\n\n**Figure caption**\n\nIberia-Biscay-Ireland Significant Wave Height extreme variability: Map of the 99th mean percentile computed from the Multi Year Product (left panel) and anomaly of the 99th percentile in 2022 computed from the Analysis product (right panel). Transparent grey areas (if any) represent regions where anomaly exceeds the climatic standard deviation (light grey) and twice the climatic standard deviation (dark grey).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00249\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Bacon S, Carter D J T. 1991. Wave climate changes in the north Atlantic and North Sea, International Journal of Climatology, 11, 545\u2013558.\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Bouws E, Jannink D, Komen GJ. 1996. The increasing wave height in the North Atlantic Ocean, Bull. Am. Met. Soc., 77, 2275\u20132277.\n* Folley M. 2017. The wave energy resource. In Pecher A, Kofoed JP (ed.), Handbook of Ocean Wave Energy, Ocean Engineering & Oceanography 7, doi:10.1007/978-3-319-39889-1_3.\n* Gleeson E, Gallagher S, Clancy C, Dias F. 2017. NAO and extreme ocean states in the Northeast Atlantic Ocean, Adv. Sci. Res., 14, 23\u201333, doi:10.5194/asr-14-23-2017.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2015. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2015 In book: .Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* Izaguirre C, M\u00e9ndez F J, Men\u00e9ndez M, Losada I J. 2011. Global extreme wave height variability based on satellite data Cristina. Geoph. Res. Letters, Vol. 38, L10607, doi: 10.1029/2011GL047302.\n* Mart\u00ednez-Asensio A, Tsimplis M N, Marcos M, Feng F, Gomis D, Jord\u00e0a G, Josey S A. 2016. Response of the North Atlantic wave climate to atmospheric modes of variability. International Journal of Climatology, 36, 1210\u20131225, doi: 10.1002/joc.4415.\n* M\u00f8rk G, Barstow S, Kabush A, Pontes MT. 2010. Assessing the global wave energy potential. Proceedings of OMAE2010 29th International Conference on Ocean, Offshore Mechanics and Arctic Engineering June 6-11, 2010, Shanghai, China.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Woolf D K, Challenor P G, Cotton P D. 2002. Variability and predictability of the North Atlantic wave climate, J. Geophys. Res., 107(C10), 3145, doi:10.1029/2001JC001124.\n* Wolf J, Woolf D K. 2006. Waves and climate change in the north-east Atlantic. Geophysical Research Letters, Vol. 33, L06604, doi: 10.1029/2005GL025113.\n* Young I R, Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548-552, doi: 10.1126/science.aav9527.\n* Kushnir Y, Cardone VJ, Greenwood JG, Cane MA. 1997. The recent increase in North Atlantic wave heights. Journal of Climate 10:2107\u20132113.\n* Marshall, J., Kushnir, Y., Battisti, D., Chang, P., Czaja, A., Dickson, R., ... & Visbeck, M. (2001). North Atlantic climate variability: phenomena, impacts and mechanisms. International Journal of Climatology: A Journal of the Royal Meteorological Society, 21(15), 1863-1898.\n", "doi": "10.48670/moi-00249", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-seastate-extreme-var-swh-mean-and-anomaly,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Significant Wave Height extreme from Reanalysis"}, "IBI_OMI_SEASTATE_swi": {"abstract": "**DEFINITION**\n\nThe Strong Wave Incidence index is proposed to quantify the variability of strong wave conditions in the Iberia-Biscay-Ireland regional seas. The anomaly of exceeding a threshold of Significant Wave Height is used to characterize the wave behavior. A sensitivity test of the threshold has been performed evaluating the differences using several ones (percentiles 75, 80, 85, 90, and 95). From this indicator, it has been chosen the 90th percentile as the most representative, coinciding with the state-of-the-art.\nTwo CMEMS products are used to compute the Strong Wave Incidence index:\n\u2022\tIBI-WAV-MYP: IBI_REANALYSIS_WAV_005_006\n\u2022\tIBI-WAV-NRT: IBI_ANALYSIS_FORECAST_WAV_005_005\nThe Strong Wave Incidence index (SWI) is defined as the difference between the climatic frequency of exceedance (Fclim) and the observational frequency of exceedance (Fobs) of the threshold defined by the 90th percentile (ThP90) of Significant Wave Height (SWH) computed on a monthly basis from hourly data of IBI-WAV-MYP product:\nSWI = Fobs(SWH > ThP90) \u2013 Fclim(SWH > ThP90)\nSince the Strong Wave Incidence index is defined as a difference of a climatic mean and an observed value, it can be considered an anomaly. Such index represents the percentage that the stormy conditions have occurred above/below the climatic average. Thus, positive/negative values indicate the percentage of hourly data that exceed the threshold above/below the climatic average, respectively.\n\n**CONTEXT**\n\nOcean waves have a high relevance over the coastal ecosystems and human activities. Extreme wave events can entail severe impacts over human infrastructures and coastal dynamics. However, the incidence of severe (90th percentile) wave events also have valuable relevance affecting the development of human activities and coastal environments. The Strong Wave Incidence index based on the CMEMS regional analysis and reanalysis product provides information on the frequency of severe wave events.\nThe IBI-MFC covers the Europe\u2019s Atlantic coast in a region bounded by the 26\u00baN and 56\u00baN parallels, and the 19\u00baW and 5\u00baE meridians. The western European coast is located at the end of the long fetch of the subpolar North Atlantic (M\u00f8rk et al., 2010), one of the world\u2019s greatest wave generating regions (Folley, 2017). Several studies have analyzed changes of the ocean wave variability in the North Atlantic Ocean (Bacon and Carter, 1991; Kursnir et al., 1997; WASA Group, 1998; Bauer, 2001; Wang and Swail, 2004; Dupuis et al., 2006; Wolf and Woolf, 2006; Dodet et al., 2010; Young et al., 2011; Young and Ribal, 2019). The observed variability is composed of fluctuations ranging from the weather scale to the seasonal scale, together with long-term fluctuations on interannual to decadal scales associated with large-scale climate oscillations. Since the ocean surface state is mainly driven by wind stresses, part of this variability in Iberia-Biscay-Ireland region is connected to the North Atlantic Oscillation (NAO) index (Bacon and Carter, 1991; Hurrell, 1995; Bouws et al., 1996, Bauer, 2001; Woolf et al., 2002; Tsimplis et al., 2005; Gleeson et al., 2017). However, later studies have quantified the relationships between the wave climate and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation pattern, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Mat\u00ednez-Asensio et al., 2016).\nThe Strong Wave Incidence index provides information on incidence of stormy events in four monitoring regions in the IBI domain. The selected monitoring regions (Figure 1.A) are aimed to provide a summarized view of the diverse climatic conditions in the IBI regional domain: Wav1 region monitors the influence of stormy conditions in the West coast of Iberian Peninsula, Wav2 region is devoted to monitor the variability of stormy conditions in the Bay of Biscay, Wav3 region is focused in the northern half of IBI domain, this region is strongly affected by the storms transported by the subpolar front, and Wav4 is focused in the influence of marine storms in the North-East African Coast, the Gulf of Cadiz and Canary Islands.\nMore details and a full scientific evaluation can be found in the CMEMS Ocean State report (Pascual et al., 2020).\n\n**CMEMS KEY FINDINGS**\n\nThe analysis of the index in the last decades do not show significant trends of the strong wave conditions over the period 1992-2021 with 99% confidence. The maximum wave event reported in region WAV1 (B) occurred in February 2014, producing an increment of 25% of strong wave conditions in the region. Two maximum wave events are found in WAV2 (C) with an increment of 15% of high wave conditions in November 2009 and February 2014. As in regions WAV1 and WAV2, in the region WAV3 (D), a strong wave event took place in February 2014, this event is one of the maximum events reported in the region with an increment of strong wave conditions of 20%, two months before (December 2013) there was a storm of similar characteristics affecting this region, other events of similar magnitude are detected on October 2000 and November 2009. The region WAV4 (E) present its maximum wave event in January 1996, such event produced a 25% of increment of strong wave conditions in the region. Despite of each monitoring region is affected by independent wave events; the analysis shows several past higher-than-average wave events that were propagated though several monitoring regions: November-December 2010 (WAV3 and WAV2); February 2014 (WAV1, WAV2, and WAV3); and February-March 2018 (WAV1 and WAV4).\nThe analysis of the NRT period (January 2022 onwards) depicts a significant event that occurred in November 2022, which affected the WAV2 and WAV3 regions, resulting in a 15% and 25% increase in maximum wave conditions, respectively. In the case of the WAV3 region, this event was the strongest event recorded in this region.\nIn the WAV4 region, an event that occurred in February 2024 was the second most intense on record, showing an 18% increase in strong wave conditions in the region.\nIn the WAV1 region, the NRT period includes two high-intensity events that occurred in February 2024 (21% increase in strong wave conditions) and April 2022 (18% increase in maximum wave conditions).\n\n**Figure caption**\n\n(A) Mean 90th percentile of Sea Wave Height computed from IBI_REANALYSIS_WAV_005_006 product at an hourly basis. Gray dotted lines denote the four monitoring areas where the Strong Wave Incidence index is computed. (B, C, D, and E) Strong Wave Incidence index averaged in monitoring regions WAV1 (A), WAV2 (B), WAV3 (C), and WAV4 (D). Panels show merged results of two CMEMS products: IBI_REANALYSIS_WAV_005_006 (blue), IBI_ANALYSIS_FORECAST_WAV_005_005 (orange). The trend and 99% confidence interval of IBI-MYP product is included (bottom right).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00251\n\n**References:**\n\n* Bacon S, Carter D J T. 1991. Wave climate changes in the north Atlantic and North Sea, International Journal of Climatology, 11, 545\u2013558.\n* Bauer E. 2001. Interannual changes of the ocean wave variability in the North Atlantic and in the North Sea, Climate Research, 18, 63\u201369.\n* Bouws E, Jannink D, Komen GJ. 1996. The increasing wave height in the North Atlantic Ocean, Bull. Am. Met. Soc., 77, 2275\u20132277.\n* Dodet G, Bertin X, Taborda R. 2010. Wave climate variability in the North-East Atlantic Ocean over the last six decades, Ocean Modelling, 31, 120\u2013131.\n* Dupuis H, Michel D, Sottolichio A. 2006. Wave climate evolution in the Bay of Biscay over two decades. Journal of Marine Systems, 63, 105\u2013114.\n* Folley M. 2017. The wave energy resource. In Pecher A, Kofoed JP (ed.), Handbook of Ocean Wave Energy, Ocean Engineering & Oceanography 7, doi:10.1007/978-3-319-39889-1_3.\n* Gleeson E, Gallagher S, Clancy C, Dias F. 2017. NAO and extreme ocean states in the Northeast Atlantic Ocean, Adv. Sci. Res., 14, 23\u201333, doi:10.5194/asr-14-23-2017.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hurrell JW. 1995. Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation, Science, 269:676\u2013679.\n* Izaguirre C, M\u00e9ndez F J, Men\u00e9ndez M, Losada I J. 2011. Global extreme wave height variability based on satellite data Cristina. Geoph. Res. Letters, Vol. 38, L10607, doi: 10.1029/2011GL047302.\n* Kushnir Y, Cardone VJ, Greenwood JG, Cane MA. 1997. The recent increase in North Atlantic wave heights. Journal of Climate 10:2107\u20132113.\n* Mart\u00ednez-Asensio A, Tsimplis M N, Marcos M, Feng F, Gomis D, Jord\u00e0a G, Josey S A. 2016. Response of the North Atlantic wave climate to atmospheric modes of variability. International Journal of Climatology, 36, 1210\u20131225, doi: 10.1002/joc.4415.\n* M\u00f8rk G, Barstow S, Kabush A, Pontes MT. 2010. Assessing the global wave energy potential. Proceedings of OMAE2010 29th International Conference on Ocean, Offshore Mechanics and Arctic Engineering June 6-11, 2010, Shanghai, China.\n* Pascual A., Levier B., Aznar R., Toledano C., Garc\u00eda-Valdecasas JM., Garc\u00eda M., Sotillo M., Aouf L., \u00c1lvarez E. (2020) Monitoring of wave sea state in the Iberia-Biscay-Ireland regional seas. In von Scuckmann et al. (2020) Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, DOI: 10.1080/1755876X.2020.1785097\n* Tsimplis M N, Woolf D K, Osborn T J, Wakelin S, Wolf J, Flather R, Shaw A G P, Woodworth P, Challenor P, Blackman D, Pert F, Yan Z, Jevrejeva S. 2005. Towards a vulnerability assessment of the UK and northern European coasts: the role of regional climate variability. Phil. Trans. R. Soc. A, Vol. 363, 1329\u20131358 doi:10.1098/rsta.2005.1571.\n* Wang X, Swail V. 2004. Historical and possible future changes of wave heights in northern hemisphere oceans. In: Perrie W (ed), Atmosphere ocean interactions, vol 2. Wessex Institute of Technology Press, Ashurst.\n* WASA-Group. 1998. Changing waves and storms in the Northeast Atlantic?, Bull. Am. Meteorol. Soc., 79:741\u2013760.\n* Wolf J, Woolf D K. 2006. Waves and climate change in the north-east Atlantic. Geophysical Research Letters, Vol. 33, L06604, doi: 10.1029/2005GL025113.\n* Woolf D K, Challenor P G, Cotton P D. 2002. Variability and predictability of the North Atlantic wave climate, J. Geophys. Res., 107(C10), 3145, doi:10.1029/2001JC001124.\n* Young I R, Zieger S, Babanin A V. 2011. Global Trends in Wind Speed and Wave Height. Science, Vol. 332, Issue 6028, 451-455, doi: 10.1126/science.1197219.\n* Young I R, Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364, 548-552, doi: 10.1126/science.aav9527.\n", "doi": "10.48670/moi-00251", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-seastate-swi,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Strong Wave Incidence index from Reanalysis"}, "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS IBI_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_PHY_005_002) and the Analysis product (IBI_ANALYSISFORECAST_PHY_005_001).\nTwo parameters have been considered for this OMI:\n\u2022\tMap of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2021).\n\u2022\tAnomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2022 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe Sea Surface Temperature is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018).\n\nThe IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The Sea Surface Temperature field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the Iberia-Biscay-Ireland region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003).\nAlso relevant in the region are the upwelling processes taking place in the coastal margins. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980).\n\n**CMEMS KEY FINDINGS**\n\nIn the IBI region, the 99th mean percentile for 1993-2021 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in \u00c1lvarez Fanjul (2019) for the period 1993-2016.\nThe analysis of the 99th percentile anomaly in the year 2023 shows that this period has been affected by a severe impact of maximum SST values. Anomalies exceeding the standard deviation affect almost the entire IBI domain, and regions impacted by thermal anomalies surpassing twice the standard deviation are also widespread below the 43\u00baN parallel.\nExtreme SST values exceeding twice the standard deviation affect not only the open ocean waters but also the easter boundary upwelling areas such as the northern half of Portugal, the Spanish Atlantic coast up to Cape Ortegal, and the African coast south of Cape Aguer.\nIt is worth noting the impact of anomalies that exceed twice the standard deviation is widespread throughout the entire Mediterranean region included in this analysis.\n\n**Figure caption**\n\nIberia-Biscay-Ireland Surface Temperature extreme variability: Map of the 99th mean percentile computed from the Multi Year Product (left panel) and anomaly of the 99th percentile in 2022 computed from the Analysis product (right panel). Transparent grey areas (if any) represent regions where anomaly exceeds the climatic standard deviation (light grey) and twice the climatic standard deviation (dark grey).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00254\n\n**References:**\n\n* Alvarez I, Gomez-Gesteira M, DeCastro M, Lorenzo MN, Crespo AJC, Dias JM. 2011. Comparative analysis of upwelling influence between the western and northern coast of the Iberian Peninsula. Continental Shelf Research, 31(5), 388-399.\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Czaja A, Frankignoul C. 2002. Observed impact of Atlantic SST anomalies on the North Atlantic Oscillation. Journal of Climate, 15(6), 606-623.\n* Dickson RR, Gurbutt PA, Pillai VN. 1980. Satellite evidence of enhanced upwelling along the European continental slope. Journal of Physical Oceanography, 10(5), 813-819.\n* Dubois C, von Schuckmann K, Josey S. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 2.9, s66\u2013s70, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Edwards A, Jones K, Graham JM, Griffiths CR, MacDougall N, Patching J, Raine R. 1996. Transient coastal upwelling and water circulation in Bantry Bay, a ria on the south-west coast of Ireland. Estuarine, Coastal and Shelf Science, 42(2), 213-230.\n* Flatau MK, Talley L, Niiler PP. 2003. The North Atlantic Oscillation, surface current velocities, and SST changes in the subpolar North Atlantic. Journal of Climate, 16(14), 2355-2369.\n* Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Br\u00f6nnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2013. Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 1.1, s5\u2013s13, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sotillo MG, Levier B, Pascual A, Gonzalez A. 2016. Iberian-Biscay-Irish Sea. In von Schuckmann et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report No.1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00254", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-tempsal-extreme-var-temp-mean-and-anomaly,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Sea Surface Temperature extreme from Reanalysis"}, "IBI_OMI_WMHE_mow": {"abstract": "**DEFINITION**\n\nVariations of the Mediterranean Outflow Water at 1000 m depth are monitored through area-averaged salinity anomalies in specifically defined boxes. The salinity data are extracted from several CMEMS products and averaged in the corresponding monitoring domain: \n* IBI-MYP: IBI_MULTIYEAR_PHY_005_002\n* IBI-NRT: IBI_ANALYSISFORECAST_PHYS_005_001\n* GLO-MYP: GLOBAL_REANALYSIS_PHY_001_030\n* CORA: INSITU_GLO_TS_REP_OBSERVATIONS_013_002_b\n* ARMOR: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012\n\nThe anomalies of salinity have been computed relative to the monthly climatology obtained from IBI-MYP. Outcomes from diverse products are combined to deliver a unique multi-product result. Multi-year products (IBI-MYP, GLO,MYP, CORA, and ARMOR) are used to show an ensemble mean and the standard deviation of members in the covered period. The IBI-NRT short-range product is not included in the ensemble, but used to provide the deterministic analysis of salinity anomalies in the most recent year.\n\n**CONTEXT**\n\nThe Mediterranean Outflow Water is a saline and warm water mass generated from the mixing processes of the North Atlantic Central Water and the Mediterranean waters overflowing the Gibraltar sill (Daniault et al., 1994). The resulting water mass is accumulated in an area west of the Iberian Peninsula (Daniault et al., 1994) and spreads into the North Atlantic following advective pathways (Holliday et al. 2003; Lozier and Stewart 2008, de Pascual-Collar et al., 2019).\nThe importance of the heat and salt transport promoted by the Mediterranean Outflow Water flow has implications beyond the boundaries of the Iberia-Biscay-Ireland domain (Reid 1979, Paillet et al. 1998, van Aken 2000). For example, (i) it contributes substantially to the salinity of the Norwegian Current (Reid 1979), (ii) the mixing processes with the Labrador Sea Water promotes a salt transport into the inner North Atlantic (Talley and MacCartney, 1982; van Aken, 2000), and (iii) the deep anti-cyclonic Meddies developed in the African slope is a cause of the large-scale westward penetration of Mediterranean salt (Iorga and Lozier, 1999).\nSeveral studies have demonstrated that the core of Mediterranean Outflow Water is affected by inter-annual variability. This variability is mainly caused by a shift of the MOW dominant northward-westward pathways (Bozec et al. 2011), it is correlated with the North Atlantic Oscillation (Bozec et al. 2011) and leads to the displacement of the boundaries of the water core (de Pascual-Collar et al., 2019). The variability of the advective pathways of MOW is an oceanographic process that conditions the destination of the Mediterranean salt transport in the North Atlantic. Therefore, monitoring the Mediterranean Outflow Water variability becomes decisive to have a proper understanding of the climate system and its evolution (e.g. Bozec et al. 2011, Pascual-Collar et al. 2019).\nThe CMEMS IBI-OMI_WMHE_mow product is aimed to monitor the inter-annual variability of the Mediterranean Outflow Water in the North Atlantic. The objective is the establishment of a long-term monitoring program to observe the variability and trends of the Mediterranean water mass in the IBI regional seas. To do that, the salinity anomaly is monitored in key areas selected to represent the main reservoir and the three main advective spreading pathways. More details and a full scientific evaluation can be found in the CMEMS Ocean State report Pascual et al., 2018 and de Pascual-Collar et al. 2019.\n\n**CMEMS KEY FINDINGS**\n\nThe absence of long-term trends in the monitoring domain Reservoir (b) suggests the steadiness of water mass properties involved on the formation of Mediterranean Outflow Water.\nResults obtained in monitoring box North (c) present an alternance of periods with positive and negative anomalies. The last negative period started in 2016 reaching up to the present. Such negative events are linked to the decrease of the northward pathway of Mediterranean Outflow Water (Bozec et al., 2011), which appears to return to steady conditions in 2020 and 2021. \nResults for box West (d) reveal a cycle of negative (2015-2017) and positive (2017 up to the present) anomalies. The positive anomalies of salinity in this region are correlated with an increase of the westward transport of salinity into the inner North Atlantic (de Pascual-Collar et al., 2019), which appear to be maintained for years 2020-2021.\nResults in monitoring boxes North and West are consistent with independent studies (Bozec et al., 2011; and de Pascual-Collar et al., 2019), suggesting a westward displacement of Mediterranean Outflow Water and the consequent contraction of the northern boundary.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00258\n\n**References:**\n\n* Bozec A, Lozier MS, Chassignet EP, Halliwell GR. 2011. On the variability of the Mediterranean outflow water in the North Atlantic from 1948 to 2006. J Geophys Res 116:C09033. doi:10.1029/2011JC007191.\n* Daniault N, Maze JP, Arhan M. 1994. Circulation and mixing of MediterraneanWater west of the Iberian Peninsula. Deep Sea Res. Part I. 41:1685\u20131714.\n* de Pascual-Collar A, Sotillo MG, Levier B, Aznar R, Lorente P, Amo-Baladr\u00f3n A, \u00c1lvarez-Fanjul E. 2019. Regional circulation patterns of Mediterranean Outflow Water near the Iberian and African continental slopes. Ocean Sci., 15, 565\u2013582. https://doi.org/10.5194/os-15-565-2019.\n* Holliday NP. 2003. Air-sea interaction and circulation changes in the northeast Atlantic. J Geophys Res. 108(C8):3259. doi:10.1029/2002JC001344.\n* Iorga MC, Lozier MS. 1999. Signatures of the Mediterranean outflow from a North Atlantic climatology: 1. Salinity and density fields. Journal of Geophysical Research: Oceans, 104(C11), 25985-26009.\n* Lozier MS, Stewart NM. 2008. On the temporally varying northward penetration of Mediterranean overflow water and eastward penetration of Labrador Sea water. J Phys Oceanogr. 38(9):2097\u20132103. doi:10.1175/2008JPO3908.1.\n* Paillet J, Arhan M, McCartney M. 1998. Spreading of labrador Sea water in the eastern North Atlantic. J Geophys Res. 103 (C5):10223\u201310239.\n* Pascual A, Levier B, Sotillo M, Verbrugge N, Aznar R, Le Cann B. 2018. Characterisation of Mediterranean outflow w\u00e1ter in the Iberia-Gulf of Biscay-Ireland region. In: von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Braseur, P., Fennel, K., Djavidnia, S.: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11, sup1, s1-s142, doi:10.1080/1755876X.2018.1489208, 2018.\n* Reid JL. 1979. On the contribution of the Mediterranean Sea outflow to the Norwegian\u2010Greenland Sea, Deep Sea Res., Part A, 26, 1199\u20131223, doi:10.1016/0198-0149(79)90064-5.\n* Talley LD, McCartney MS. 1982. Distribution and circulation of Labrador Sea water. Journal of Physical Oceanography, 12(11), 1189-1205.\n* van Aken HM. 2000. The hydrography of the mid-latitude northeast Atlantic Ocean I: the deep water masses. Deep Sea Res. Part I. 47:757\u2013788.\n", "doi": "10.48670/moi-00258", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-wmhe-mow,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterrranean Outflow Water Index from Reanalysis & Multi-Observations Reprocessing"}, "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031": {"abstract": "Arctic Oceans - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00031", "doi": "10.48670/moi-00031", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-arc-phybgcwav-discrete-mynrt-013-031,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean- In Situ Near Real Time Observations"}, "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032": {"abstract": "Baltic Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00032", "doi": "10.48670/moi-00032", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-bal-phybgcwav-discrete-mynrt-013-032,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea- In Situ Near Real Time Observations"}, "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034": {"abstract": "Black Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00033", "doi": "10.48670/moi-00033", "instrument": null, "keywords": "black-sea,coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-blk-phybgcwav-discrete-mynrt-013-034,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea- In-Situ Near Real Time Observations"}, "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050": {"abstract": "Global Ocean- in-situ reprocessed Carbon observations. This product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: Surface Ocean Carbon ATlas SOCATv2024 and GLobal Ocean Data Analysis Project GLODAPv2.2023. \nThe SOCATv2024-OBS dataset contains >38 million observations of fugacity of CO2 of the surface global ocean from 1957 to early 2024. The quality control procedures are described in Bakker et al. (2016). These observations form the basis of the gridded products included in SOCATv2024-GRIDDED: monthly, yearly and decadal averages of fCO2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean.\nGLODAPv2.2023-OBS contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and pH from 1972 to 2021. These data were subjected to an extensive quality control and bias correction described in Olsen et al. (2020). GLODAPv2-GRIDDED contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and pH over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous major iteration of GLODAP, GLODAPv2. \nSOCAT and GLODAP are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles (see References below) in order to support future sustainability of the data products.\n\n**DOI (product):** \nhttps://doi.org/10.17882/99089\n\n**References:**\n\n* Bakker et al., 2016. A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT). Earth Syst. Sci. Data, 8, 383\u2013413, https://doi.org/10.5194/essd-8-383-2016.\n* Lauvset et al. 2024. The annual update GLODAPv2.2023: the global interior ocean biogeochemical data product. Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-468.\n* Lauvset et al., 2016. A new global interior ocean mapped climatology: t\u202f\u00d7\u2009\u202f1\u00b0 GLODAP version 2. Earth Syst. Sci. Data, 8, 325\u2013340, https://doi.org/10.5194/essd-8-325-2016.\n", "doi": "10.17882/99089", "instrument": null, "keywords": "coastal-marine-environment,fugacity-of-carbon-dioxide-in-sea-water,global-ocean,in-situ-observation,insitu-glo-bgc-carbon-discrete-my-013-050,level-3,marine-resources,marine-safety,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,multi-year,oceanographic-geographical-features,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1957-10-22T22:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - In Situ reprocessed carbon observations - SOCATv2024 / GLODAPv2.2023"}, "INSITU_GLO_BGC_DISCRETE_MY_013_046": {"abstract": "For the Global Ocean- In-situ observation delivered in delayed mode. This In Situ delayed mode product integrates the best available version of in situ oxygen, chlorophyll / fluorescence and nutrients data.\n\n**DOI (product):** \nhttps://doi.org/10.17882/86207", "doi": "10.17882/86207", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-bgc-discrete-my-013-046,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-silicate-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,multi-year,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1841-03-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Delayed Mode Biogeochemical product"}, "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030": {"abstract": "Global Ocean - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average. Data are collected mainly through global networks (Argo, OceanSites, GOSUD, EGO) and through the GTS\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00036", "doi": "10.48670/moi-00036", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,global-ocean,in-situ-observation,insitu-glo-phybgcwav-discrete-mynrt-013-030,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- In-Situ Near-Real-Time Observations"}, "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053": {"abstract": "This product integrates sea level observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from the Global telecommunication system (GTS) used by the Met Offices.\n\n**DOI (product):** \nhttps://doi.org/10.17882/93670", "doi": "10.17882/93670", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ssh-discrete-my-013-053,level-2,marine-resources,marine-safety,near-real-time,non-tidal-elevation-of-sea-surface-height,oceanographic-geographical-features,tidal-sea-surface-height-above-reference-datum,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1821-05-25T05:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Delayed Mode Sea level product"}, "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001": {"abstract": "For the Global Ocean- In-situ observation yearly delivery in delayed mode. The In Situ delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for temperature and salinity measurements. These data are collected from main global networks (Argo, GOSUD, OceanSITES, World Ocean Database) completed by European data provided by EUROGOOS regional systems and national system by the regional INS TAC components. It is updated on a yearly basis. This version is a merged product between the previous verion of CORA and EN4 distributed by the Met Office for the period 1950-1990.\n\n**DOI (product):** \nhttps://doi.org/10.17882/46219", "doi": "10.17882/46219", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ts-discrete-my-013-001,level-2,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- CORA- In-situ Observations Yearly Delivery in Delayed Mode"}, "INSITU_GLO_PHY_TS_OA_MY_013_052": {"abstract": "Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the reprocessed in-situ global product CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) using the ISAS software. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n**DOI (product):** \nhttps://doi.org/10.17882/46219", "doi": "10.17882/46219", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ts-oa-my-013-052,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1960-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode"}, "INSITU_GLO_PHY_TS_OA_NRT_013_002": {"abstract": "For the Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the in-situ near real time database are produced monthly. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a support for localized experience (cruises), providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00037", "doi": "10.48670/moi-00037", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-phy-ts-oa-nrt-013-002,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- Real time in-situ observations objective analysis"}, "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044": {"abstract": "Global Ocean - This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface and subsurface currents. Current data from 5 different types of instruments are distributed:\n* The drifter's near-surface velocities computed from their position measurements. In addition, a wind slippage correction is provided from 1993. Information on the presence of the drogue of the drifters is also provided.\n* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency (HF) radars that are part of the European HF radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata.\n* The zonal and meridional velocities, at parking depth (mostly around 1000m) and at the surface, calculated along the trajectories of the floats which are part of the Argo Program.\n* The velocity profiles within the water column coming from Acoustic Doppler Current Profiler (vessel mounted ADCP, Moored ADCP, saildrones) platforms\n* The near-surface and subsurface velocities calculated from gliders (autonomous underwater vehicle) trajectories\n\n**DOI (product):**\nhttps://doi.org/10.17882/86236", "doi": "10.17882/86236", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,insitu-glo-phy-uv-discrete-my-013-044,level-2,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,oceanographic-geographical-features,sea-water-temperature,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1979-12-11T04:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048": {"abstract": "This product is entirely dedicated to ocean current data observed in near-real time. Current data from 3 different types of instruments are distributed:\n* The near-surface zonal and meridional velocities calculated along the trajectories of the drifting buoys which are part of the DBCP\u2019s Global Drifter Program. These data are delivered together with wind stress components, surface temperature and a wind-slippage correction for drogue-off and drogue-on drifters trajectories. \n* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency radars that are part of the European High Frequency radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata.\n* The zonal and meridional velocities, at parking depth and in surface, calculated along the trajectories of the floats which are part of the Argo Program.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00041", "doi": "10.48670/moi-00041", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,insitu-glo-phy-uv-discrete-nrt-013-048,level-2,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,oceanographic-geographical-features,sea-water-temperature,surface-eastward-sea-water-velocity,surface-northward-sea-water-velocity,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1986-06-02T09:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean- in-situ Near real time observations of ocean currents"}, "INSITU_GLO_WAV_DISCRETE_MY_013_045": {"abstract": "These products integrate wave observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from National Data Centers (NODCs) and JCOMM global systems (OceanSITES, DBCP) and the Global telecommunication system (GTS) used by the Met Offices.\n\n**DOI (product):** \nhttps://doi.org/10.17882/70345", "doi": "10.17882/70345", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,insitu-glo-wav-discrete-my-013-045,level-2,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,sea-surface-wave-mean-period,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-04-27T18:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Delayed Mode Wave product"}, "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033": {"abstract": "IBI Seas - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00043", "doi": "10.48670/moi-00043", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,iberian-biscay-irish-seas,in-situ-observation,insitu-ibi-phybgcwav-discrete-mynrt-013-033,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Iberian Biscay Irish Ocean- In-Situ Near Real Time Observations"}, "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035": {"abstract": "Mediterranean Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00044", "doi": "10.48670/moi-00044", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-med-phybgcwav-discrete-mynrt-013-035,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea- In-Situ Near Real Time Observations"}, "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036": {"abstract": "NorthWest Shelf area - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00045", "doi": "10.48670/moi-00045", "instrument": null, "keywords": "coastal-marine-environment,direction-of-sea-water-velocity,in-situ-observation,insitu-nws-phybgcwav-discrete-mynrt-013-036,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-01-28T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 2", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Ocean In-Situ Near Real Time observations"}, "MEDSEA_ANALYSISFORECAST_BGC_006_014": {"abstract": "The biogeochemical analysis and forecasts for the Mediterranean Sea at 1/24\u00b0 of horizontal resolution (ca. 4 km) are produced by means of the MedBFM4 model system. MedBFM4, which is run by OGS (IT), consists of the coupling of the multi-stream atmosphere radiative model OASIM, the multi-stream in-water radiative and tracer transport model OGSTM_BIOPTIMOD v4.6, and the biogeochemical flux model BFM v5.3. Additionally, MedBFM4 features the 3D variational data assimilation scheme 3DVAR-BIO v4.1 with the assimilation of surface chlorophyll (CMEMS-OCTAC NRT product) and of vertical profiles of chlorophyll, nitrate and oxygen (BGC-Argo floats provided by CORIOLIS DAC). The biogeochemical MedBFM system, which is forced by the NEMO-OceanVar model (MEDSEA_ANALYSIS_FORECAST_PHY_006_013), produces one day of hindcast and ten days of forecast (every day) and seven days of analysis (weekly on Tuesday).\nSalon, S.; Cossarini, G.; Bolzon, G.; Feudale, L.; Lazzari, P.; Teruzzi, A.; Solidoro, C., and Crise, A. (2019) Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. Ocean Science, 15, pp.997\u20131022. DOI: https://doi.org/10.5194/os-15-997-2019\n\n_DOI (Product)_: \nhttps://doi.org/10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbfm4\n\n**References:**\n\n* Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Teruzzi, A., Di Biagio, V., Coidessa, G., Alvarez, E., Amadio, C., & Cossarini, G. (2022). Mediterranean Sea Biogeochemical Analysis and Forecast (CMEMS MED-Biogeochemistry, MedBFM4 system) (Version 1) [Data set]. Copernicus Marine Service. https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_BGC_006_014_MEDBFM4\n", "doi": "10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbfm4", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,medsea-analysisforecast-bgc-006-014,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "MEDSEA_ANALYSISFORECAST_PHY_006_013": {"abstract": "The physical component of the Mediterranean Forecasting System (Med-Physics) is a coupled hydrodynamic-wave model implemented over the whole Mediterranean Basin including tides. The model horizontal grid resolution is 1/24\u02da (ca. 4 km) and has 141 unevenly spaced vertical levels.\nThe hydrodynamics are supplied by the Nucleous for European Modelling of the Ocean NEMO (v4.2) and include the representation of tides, while the wave component is provided by Wave Watch-III (v6.07) coupled through OASIS; the model solutions are corrected by a 3DVAR assimilation scheme (OceanVar) for temperature and salinity vertical profiles and along track satellite Sea Level Anomaly observations. \n\n_Product Citation_: Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\"\n\n_DOI (Product)_:\nhttps://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8\n\n**References:**\n\n* Clementi, E., Aydogdu, A., Goglio, A. C., Pistoia, J., Escudier, R., Drudi, M., Grandi, A., Mariani, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Coppini, G., Masina, S., & Pinardi, N. (2021). Mediterranean Sea Physical Analysis and Forecast (CMEMS MED-Currents, EAS6 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8\n", "doi": "10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-analysisforecast-phy-006-013,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Physics Analysis and Forecast"}, "MEDSEA_ANALYSISFORECAST_WAV_006_017": {"abstract": "MEDSEA_ANALYSISFORECAST_WAV_006_017 is the nominal wave product of the Mediterranean Sea Forecasting system, composed by hourly wave parameters at 1/24\u00ba horizontal resolution covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The waves forecast component (Med-WAV system) is a wave model based on the WAM Cycle 6. The Med-WAV modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins and the model solutions are corrected by an optimal interpolation data assimilation scheme of all available along track satellite significant wave height observations. The atmospheric forcing is provided by the operational ECMWF Numerical Weather Prediction model and the wave model is forced with hourly averaged surface currents and sea level obtained from MEDSEA_ANALYSISFORECAST_PHY_006_013 at 1/24\u00b0 resolution. The model uses wave spectra for Open Boundary Conditions from GLOBAL_ANALYSIS_FORECAST_WAV_001_027 product. The wave system includes 2 forecast cycles providing twice per day a Mediterranean wave analysis and 10 days of wave forecasts.\n\n_Product Citation_: Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n**DOI (product)**: https://doi.org/10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4\n\n**References:**\n\n* Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2023). Mediterranean Sea Waves Analysis and Forecast (Copernicus Marine Service MED-Waves, MEDWA\u039c4 system) (Version 1) [Data set]. Copernicus Marine Service (CMS). https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4\n", "doi": "10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4", "instrument": null, "keywords": "coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-analysisforecast-wav-006-017,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-04-19T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Waves Analysis and Forecast"}, "MEDSEA_MULTIYEAR_BGC_006_008": {"abstract": "The Mediterranean Sea biogeochemical reanalysis at 1/24\u00b0 of horizontal resolution (ca. 4 km) covers the period from Jan 1999 to 1 month to the present and is produced by means of the MedBFM3 model system. MedBFM3, which is run by OGS (IT), includes the transport model OGSTM v4.0 coupled with the biogeochemical flux model BFM v5 and the variational data assimilation module 3DVAR-BIO v2.1 for surface chlorophyll. MedBFM3 is forced by the physical reanalysis (MEDSEA_MULTIYEAR_PHY_006_004 product run by CMCC) that provides daily forcing fields (i.e., currents, temperature, salinity, diffusivities, wind and solar radiation). The ESA-CCI database of surface chlorophyll concentration (CMEMS-OCTAC REP product) is assimilated with a weekly frequency. \n\nCossarini, G., Feudale, L., Teruzzi, A., Bolzon, G., Coidessa, G., Solidoro C., Amadio, C., Lazzari, P., Brosich, A., Di Biagio, V., and Salon, S., 2021. High-resolution reanalysis of the Mediterranean Sea biogeochemistry (1999-2019). Frontiers in Marine Science. Front. Mar. Sci. 8:741486.doi: 10.3389/fmars.2021.741486\n\n_Product Citation_: Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n_DOI (Product)_: https://doi.org/10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3\n\n_DOI (Interim dataset)_:\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I\n\n**References:**\n\n* Teruzzi, A., Di Biagio, V., Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Coidessa, G., & Cossarini, G. (2021). Mediterranean Sea Biogeochemical Reanalysis (CMEMS MED-Biogeochemistry, MedBFM3 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3\n* Teruzzi, A., Feudale, L., Bolzon, G., Lazzari, P., Salon, S., Di Biagio, V., Coidessa, G., & Cossarini, G. (2021). Mediterranean Sea Biogeochemical Reanalysis INTERIM (CMEMS MED-Biogeochemistry, MedBFM3i system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS) https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I\n", "doi": "10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,medsea-multiyear-bgc-006-008,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "MEDSEA_MULTIYEAR_PHY_006_004": {"abstract": "The Med MFC physical multiyear product is generated by a numerical system composed of an hydrodynamic model, supplied by the Nucleous for European Modelling of the Ocean (NEMO) and a variational data assimilation scheme (OceanVAR) for temperature and salinity vertical profiles and satellite Sea Level Anomaly along track data. It contains a reanalysis dataset and an interim dataset which covers the period after the reanalysis until 1 month before present. The model horizontal grid resolution is 1/24\u02da (ca. 4-5 km) and the unevenly spaced vertical levels are 141. \n\n**Product Citation**: \nPlease refer to our Technical FAQ for citing products\n\n**DOI (Product)**: \nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n\n**DOI (Interim dataset)**:\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I\n\n**References:**\n\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., Omar, M., Masina, S., Coppini G., Pinardi, N. (2021). A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, 9, 1060, https://www.frontiersin.org/article/10.3389/feart.2021.702285, DOI=10.3389/feart.2021.702285\n* Nigam, T., Escudier, R., Pistoia, J., Aydogdu, A., Omar, M., Clementi, E., Cipollone, A., Drudi, M., Grandi, A., Mariani, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2021). Mediterranean Sea Physical Reanalysis INTERIM (CMEMS MED-Currents, E3R1i system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I\n", "doi": "10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-multiyear-phy-006-004,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-downward-heat-flux-in-sea-water,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,surface-water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1987-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Physics Reanalysis"}, "MEDSEA_MULTIYEAR_WAV_006_012": {"abstract": "MEDSEA_MULTIYEAR_WAV_006_012 is the multi-year wave product of the Mediterranean Sea Waves forecasting system (Med-WAV). It contains a Reanalysis dataset, an Interim dataset covering the period after the reanalysis until 1 month before present and a monthly climatological dataset (reference period 1993-2016). The Reanalysis dataset is a multi-year wave reanalysis starting from January 1985, composed by hourly wave parameters at 1/24\u00b0 horizontal resolution, covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The Med-WAV modelling system is based on wave model WAM 4.6.2 and has been developed as a nested sequence of two computational grids (coarse and fine) to ensure that swell propagating from the North Atlantic (NA) towards the strait of Gibraltar is correctly entering the Mediterranean Sea. The coarse grid covers the North Atlantic Ocean from 75\u00b0W to 10\u00b0E and from 70\u00b0 N to 10\u00b0 S in 1/6\u00b0 resolution while the nested fine grid covers the Mediterranean Sea from 18.125\u00b0 W to 36.2917\u00b0 E and from 30.1875\u00b0 N to 45.9792\u00b0 N with a 1/24\u00b0 resolution. The modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins. The wave system also includes an optimal interpolation assimilation scheme assimilating significant wave height along track satellite observations available through CMEMS and it is forced with daily averaged currents from Med-Physics and with 1-h, 0.25\u00b0 horizontal-resolution ERA5 reanalysis 10m-above-sea-surface winds from ECMWF. \n\n_Product Citation_: Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n_DOI (Product)_: https://doi.org/10.25423/cmcc/medsea_multiyear_wav_006_012 \n\n_DOI (Interim dataset)_: \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I \n \n_DOI (climatological dataset)_: \nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_CLIM \n\n**DOI (Interim dataset)**:\nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I\n\n**References:**\n\n* Korres, G., Ravdas, M., Denaxa, D., & Sotiropoulou, M. (2021). Mediterranean Sea Waves Reanalysis INTERIM (CMEMS Med-Waves, MedWAM3I system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I\n* Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2023). Mediterranean Sea Waves Monthly Climatology (CMS Med-Waves, MedWAM3 system) (Version 1) [Data set]. Copernicus Marine Service (CMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012_CLIM\n* Korres, G., Ravdas, M., Zacharioudaki, A., Denaxa, D., & Sotiropoulou, M. (2021). Mediterranean Sea Waves Reanalysis (CMEMS Med-Waves, MedWAM3 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012\n", "doi": "10.25423/cmcc/medsea_multiyear_wav_006_012", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,medsea-multiyear-wav-006-012,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Waves Reanalysis"}, "MEDSEA_OMI_OHC_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 700 m depth:\nOHC=\u222b_(z_1)^(z_2)\u03c1_0 c_p (T_yr-T_clim )dz \t\t\t\t\t\t\t\t[1]\nwith a reference density of = 1030 kgm-3 and a specific heat capacity of cp = 3980 J kg-1 \u00b0C-1 (e.g. von Schuckmann et al., 2009).\nTime series of annual mean values area averaged ocean heat content is provided for the Mediterranean Sea (30\u00b0N, 46\u00b0N; 6\u00b0W, 36\u00b0E) and is evaluated for topography deeper than 300m.\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future.\nThe quality evaluation of MEDSEA_OMI_OHC_area_averaged_anomalies is based on the \u201cmulti-product\u201d approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean\u2019s experience (Masina et al., 2017). \nSix global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:\n\tThe Mediterranean Sea Reanalysis at 1/24 degree horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)\n\tFour global reanalyses at 1/4 degree horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031): \nGLORYS, C-GLORS, ORAS5, FOAM\n\tTwo observation based products: \nCORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) and \nARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). \nDetails on the products are delivered in the PUM and QUID of this OMI. \n\n**CMEMS KEY FINDINGS**\n\nThe ensemble mean ocean heat content anomaly time series over the Mediterranean Sea shows a continuous increase in the period 1993-2019 at rate of 1.4\u00b10.3 W/m2 in the upper 700m. After 2005 the rate has clearly increased with respect the previous decade, in agreement with Iona et al. (2018).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00261\n\n**References:**\n\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Iona, A., A. Theodorou, S. Sofianos, S. Watelet, C. Troupin, J.-M. Beckers, 2018: Mediterranean Sea climatic indices: monitoring long term variability and climate changes, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2018-51, in review.\n* Masina S., A. Storto, N. Ferry, M. Valdivieso, K. Haines, M. Balmaseda, H. Zuo, M. Drevillon, L. Parent, 2017: An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Climate Dynamics, 49 (3): 813-841. DOI: 10.1007/s00382-015-2728-5\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann et al., 2018: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:sup1, s1-s142, DOI: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00261", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,medsea-omi-ohc-area-averaged-anomalies,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Ocean Heat Content Anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS MEDSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_WAV_006_012) and the Analysis product (MEDSEA_ANALYSIS_FORECAST_WAV_006_017).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020.\nThis indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (P\u00e9rez G\u00f3mez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and \u00c1lvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (\u00c1lvarez- Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (Gonz\u00e1lez-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2014). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions.\nThe Mediterranean Sea is an almost enclosed basin where the complexity of its orographic characteristics deeply influences the atmospheric circulation at local scale, giving rise to strong regional wind regimes (Drobinski et al. 2018). Therefore, since waves are primarily driven by winds, high waves are present over most of the Mediterranean Sea and tend to reach the highest values where strong wind and long fetch (i.e. the horizontal distance over which wave-generating winds blow) are simultaneously present (Lionello et al. 2006). Specifically, as seen in figure and in agreement with other studies (e.g. Sartini et al. 2017), the highest values (5 \u2013 6 m in figure, top) extend from the Gulf of Lion to the southwestern Sardinia through the Balearic Sea and are sustained southwards approaching the Algerian coast. They result from northerly winds dominant in the western Mediterranean Sea (Mistral or Tramontana), that become stronger due to orographic effects (Menendez et al. 2014), and act over a large area. In the Ionian Sea, the northerly Mistral wind is still the main cause of high waves (4-5 m in figure, top). In the Aegean and Levantine Seas, high waves (4-5 m in figure, top) are caused by the northerly Bora winds, prevalent in winter, and the northerly Etesian winds, prevalent in summer (Lionello et al. 2006; Chronis et al. 2011; Menendez et al. 2014). In general, northerly winds are responsible for most high waves in the Mediterranean (e.g. Chronis et al. 2011; Menendez et al. 2014). In agreement with figure (top), studies on the eastern Mediterranean and the Hellenic Seas have found that the typical wave height range in the Aegean Sea is similar to the one observed in the Ionian Sea despite the shorter fetches characterizing the former basin (Zacharioudaki et al. 2015). This is because of the numerous islands in the Aegean Sea which cause wind funneling and enhance the occurrence of extreme winds and thus of extreme waves (Kotroni et al. 2001). Special mention should be made of the high waves, sustained throughout the year, observed east and west of the island of Crete, i.e. around the exiting points of the northerly airflow in the Aegean Sea (Zacharioudaki et al. 2015). This airflow is characterized by consistently high magnitudes that are sustained during all seasons in contrast to other airflows in the Mediterranean Sea that exhibit a more pronounced seasonality (Chronis et al. 2011). \n\n**CMEMS KEY FINDINGS**\n\nIn 2020 (bottom panel), higher-than-average values of the 99th percentile of Significant Wave Height are seen over most of the northern Mediterranean Sea, in the eastern Alboran Sea, and along stretches of the African coast (Tunisia, Libya and Egypt). In many cases they exceed the climatic standard deviation. Regions where the climatic standard deviation is exceeded twice are the European and African coast of the eastern Alboran Sea, a considerable part of the eastern Spanish coast, the Ligurian Sea and part of the east coast of France as well as areas of the southern Adriatic. These anomalies correspond to the maximum positive anomalies computed in the Mediterranean Sea for year 2020 with values that reach up to 1.1 m. Spatially constrained maxima are also found at other coastal stretches (e.g. Algeri, southeast Sardinia). Part of the positive anomalies found along the French and Spanish coast, including the coast of the Balearic Islands, can be associated with the wind storm \u201cGloria\u201d (19/1 \u2013 24/1) during which exceptional eastern winds originated in the Ligurian Sea and propagated westwards. The storm, which was of a particularly high intensity and long duration, caused record breaking wave heights in the region, and, in return, great damage to the coast (Amores et al., 2020; de Alfonso et al., 2021). Other storms that could have contributed to the positive anomalies observed in the western Mediterranean Sea include: storm Karine (25/2 \u2013 5/4), which caused high waves from the eastern coast of Spain to the Balearic Islands (Copernicus, Climate Change Service, 2020); storm Bernardo (7/11 \u2013 18/11) which also affected the Balearic islands and the Algerian coast and; storm Herv\u00e9 (2/2 \u2013 8/2) during which the highest wind gust was recorded at north Corsica (Wikiwand, 2021). In the eastern Mediterranean Sea, the medicane Ianos (14/9 \u2013 21/9) may have contributed to the positive anomalies shown in the central Ionian Sea since this area coincides with the area of peak wave height values during the medicane (Copernicus, 2020a and Copernicus, 2020b). Otherwise, higher-than-average values in the figure are the result of severe, yet not unusual, wind events, which occurred during the year. Negative anomalies occur over most of the southern Mediterranean Sea, east of the Alboran Sea. The maximum negative anomalies reach about -1 m and are located in the southeastern Ionian Sea and west of the south part of mainland Greece as well as in coastal locations of the north and east Aegean They appear to be quite unusual since they are greater than two times the climatic standard deviation in the region. They could imply less severe southerly wind activity during 2020 (Drobinski et al., 2018). \n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00262\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Amores, A., Marcos, M., Carri\u00f3, Di.S., Gomez-Pujol, L., 2020. Coastal impacts of Storm Gloria (January 2020) over the north-western Mediterranean. Nat. Hazards Earth Syst. Sci. 20, 1955\u20131968. doi:10.5194/nhess-20-1955-2020\n* Chronis T, Papadopoulos V, Nikolopoulos EI. 2011. QuickSCAT observations of extreme wind events over the Mediterranean and Black Seas during 2000-2008. Int J Climatol. 31: 2068\u20132077.\n* Copernicus: Climate Change Service. 2020a (Last accessed July 2021): URL: https://surfobs.climate.copernicus.eu/stateoftheclimate/march2020.php\n* Copernicus, Copernicus Marine Service. 2020b (Last accessed July 2021): URL: https://marine.copernicus.eu/news/following-cyclone-ianos-across-mediterranean-sea\n* de Alfonso, M., Lin-Ye, J., Garc\u00eda-Valdecasas, J.M., P\u00e9rez-Rubio, S., Luna, M.Y., Santos-Mu\u00f1oz, D., Ruiz, M.I., P\u00e9rez-G\u00f3mez, B., \u00c1lvarez-Fanjul, E., 2021. Storm Gloria: Sea State Evolution Based on in situ Measurements and Modeled Data and Its Impact on Extreme Values. Front. Mar. Sci. 8, 1\u201317. doi:10.3389/fmars.2021.646873\n* Drobinski P, Alpert P, Cavicchia L, Flaoumas E, Hochman A, Kotroni V. 2018. Strong winds Observed trends, future projections, Sub-chapter 1.3.2, p. 115-122. In: Moatti JP, Thi\u00e9bault S (dir.). The Mediterranean region under climate change: A scientific update. Marseille: IRD \u00c9ditions.\n* Gonz\u00e1lez-Marco D, Sierra J P, Ybarra O F, S\u00e1nchez-Arcilla A. 2008. Implications of long waves in harbor management: The Gij\u00f3n port case study. Ocean & Coastal Management, 51, 180-201. doi:10.1016/j.ocecoaman.2007.04.001.\n* Hanson et al., 2014. Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society January 2014 In book: Coastal and Marine Hazards, Risks, and Disasters Edition: Hazards and Disasters Series, Elsevier Major Reference Works Chapter: Chapter 11: Extreme Waves: Causes, Characteristics and Impact on Coastal Environments and Society. Publisher: Elsevier Editors: Ellis, J and Sherman, D. J.\n* Hewit J E, Cummings V J, Elis J I, Funnell G, Norkko A, Talley T S, Thrush S.F. 2003. The role of waves in the colonisation of terrestrial sediments deposited in the marine environment. Journal of Experimental marine Biology and Ecology, 290, 19-47, doi:10.1016/S0022-0981(03)00051-0.\n* Kotroni V, Lagouvardos K, Lalas D. 2001. The effect of the island of Crete on the Etesian winds over the Aegean Sea. Q J R Meteorol Soc. 127: 1917\u20131937. doi:10.1002/qj.49712757604\n* Lionello P, Rizzoli PM, Boscolo R. 2006. Mediterranean climate variability, developments in earth and environmental sciences. Elsevier.\n* Menendez M, Garc\u00eda-D\u00edez M, Fita L, Fern\u00e1ndez J, M\u00e9ndez FJ, Guti\u00e9rrez JM. 2014. High-resolution sea wind hindcasts over the Mediterranean area. Clim Dyn. 42:1857\u20131872.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sartini L, Besio G, Cassola F. 2017. Spatio-temporal modelling of extreme wave heights in the Mediterranean Sea. Ocean Modelling, 117, 52-69.\n* Savina H, Lefevre J-M, Josse P, Dandin P. 2003. Definition of warning criteria. Proceedings of MAXWAVE Final Meeting, October 8-11, Geneva, Switzerland.\n* Wikiwand: 2019 - 20 European windstorm season. URL: https://www.wikiwand.com/en/2019%E2%80%9320_European_windstorm_season\n* Zacharioudaki A, Korres G, Perivoliotis L, 2015. Wave climate of the Hellenic Seas obtained from a wave hindcast for the period 1960\u20132001. Ocean Dynamics. 65: 795\u2013816. https://doi.org/10.1007/s10236-015-0840-z\n", "doi": "10.48670/moi-00262", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-seastate-extreme-var-swh-mean-and-anomaly,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Significant Wave Height extreme from Reanalysis"}, "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS MEDSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_PHY_006_004) and the Analysis product (MEDSEA_ANALYSISFORECAST_PHY_006_013).\nTwo parameters have been considered for this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1987-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Near Real Time product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\n**CONTEXT**\n\nThe Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. In recent decades (from mid \u201880s) the Mediterranean Sea showed a trend of increasing temperatures (Ducrocq et al., 2016), which has been observed also by means of the CMEMS SST_MED_SST_L4_REP_OBSERVATIONS_010_021 satellite product and reported in the following CMEMS OMI: MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies and MEDSEA_OMI_TEMPSAL_sst_trend.\nThe Mediterranean Sea is a semi-enclosed sea characterized by an annual average surface temperature which varies horizontally from ~14\u00b0C in the Northwestern part of the basin to ~23\u00b0C in the Southeastern areas. Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions. The Mediterranean Sea annual 99th percentile presents a significant interannual and multidecadal variability with a significant increase starting from the 80\u2019s as shown in Marb\u00e0 et al. (2015) which is also in good agreement with the multidecadal change of the mean SST reported in Mariotti et al. (2012). Moreover the spatial variability of the SST 99th percentile shows large differences at regional scale (Darmariaki et al., 2019; Pastor et al. 2018).\n\n**CMEMS KEY FINDINGS**\n\nThe Mediterranean mean Sea Surface Temperature 99th percentile evaluated in the period 1987-2019 (upper panel) presents highest values (~ 28-30 \u00b0C) in the eastern Mediterranean-Levantine basin and along the Tunisian coasts especially in the area of the Gulf of Gabes, while the lowest (~ 23\u201325 \u00b0C) are found in the Gulf of Lyon (a deep water formation area), in the Alboran Sea (affected by incoming Atlantic waters) and the eastern part of the Aegean Sea (an upwelling region). These results are in agreement with previous findings in Darmariaki et al. (2019) and Pastor et al. (2018) and are consistent with the ones presented in CMEMS OSR3 (Alvarez Fanjul et al., 2019) for the period 1993-2016.\nThe 2020 Sea Surface Temperature 99th percentile anomaly map (bottom panel) shows a general positive pattern up to +3\u00b0C in the North-West Mediterranean area while colder anomalies are visible in the Gulf of Lion and North Aegean Sea . This Ocean Monitoring Indicator confirms the continuous warming of the SST and in particular it shows that the year 2020 is characterized by an overall increase of the extreme Sea Surface Temperature values in almost the whole domain with respect to the reference period. This finding can be probably affected by the different dataset used to evaluate this anomaly map: the 2020 Sea Surface Temperature 99th percentile derived from the Near Real Time Analysis product compared to the mean (1987-2019) Sea Surface Temperature 99th percentile evaluated from the Reanalysis product which, among the others, is characterized by different atmospheric forcing).\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00266\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Darmaraki S, Somot S, Sevault F, Nabat P, Cabos W, Cavicchia L, et al. 2019. Future evolution of marine heatwaves in the Mediterranean Sea. Clim. Dyn. 53, 1371\u20131392. doi: 10.1007/s00382-019-04661-z\n* Ducrocq V., Drobinski P., Gualdi S., Raimbault P. 2016. The water cycle in the Mediterranean. Chapter 1.2.1 in The Mediterranean region under climate change. IRD E\u0301ditions. DOI : 10.4000/books.irdeditions.22908.\n* Marb\u00e0 N, Jord\u00e0 G, Agust\u00ed S, Girard C, Duarte CM. 2015. Footprints of climate change on Mediterranean Sea biota. Front.Mar.Sci.2:56. doi: 10.3389/fmars.2015.00056\n* Mariotti A and Dell\u2019Aquila A. 2012. Decadal climate variability in the Mediterranean region: roles of large-scale forcings and regional processes. Clim Dyn. 38,1129\u20131145. doi:10.1007/s00382-011-1056-7\n* Pastor F, Valiente JA, Palau JL. 2018. Sea Surface Temperature in the Mediterranean: Trends and Spatial Patterns (1982\u20132016). Pure Appl. Geophys, 175: 4017. https://doi.org/10.1007/s00024-017-1739-zP\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Buongiorno Nardelli B. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sens. 2020, 12, 132.\n", "doi": "10.48670/moi-00266", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-tempsal-extreme-var-temp-mean-and-anomaly,multi-year,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Surface Temperature extreme from Reanalysis"}, "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe medsea_omi_tempsal_sst_area_averaged_anomalies product for 2023 includes unfiltered Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the Mediterranean Sea, and 24-month filtered SST anomalies, obtained by using the X11-seasonal adjustment procedure (see e.g. Pezzulli et al., 2005; Pisano et al., 2020). This OMI is derived from the CMEMS Reprocessed Mediterranean L4 SST satellite product (SST_MED_SST_L4_REP_OBSERVATIONS_010_021, see also the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf), which provides the SSTs used to compute the evolution of SST anomalies (unfiltered and filtered) over the Mediterranean Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Mediterranean Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Anomalies are computed against the 1991-2020 reference period. The 30-year climatology 1991-2020 is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). The Mediterranean Sea is a climate change hotspot (Giorgi F., 2006). Indeed, Mediterranean SST has experienced a continuous warming trend since the beginning of 1980s (e.g., Pisano et al., 2020; Pastor et al., 2020). Specifically, since the beginning of the 21st century (from 2000 onward), the Mediterranean Sea featured the highest SSTs and this warming trend is expected to continue throughout the 21st century (Kirtman et al., 2013). \n\n**KEY FINDINGS**\n\nDuring 2023, the Mediterranean Sea continued experiencing the intense sea surface temperatures\u2019 warming (marine heatwave event) that started in May 2022 (Marullo et al., 2023). The basin average SST anomaly was 0.9 \u00b1 0.1 \u00b0C in 2023, the highest in this record. The Mediterranean SST warming started in May 2022, when the mean anomaly increased abruptly from 0.01 \u00b0C (April) to 0.76 \u00b0C (May), reaching the highest values during June (1.66 \u00b0C) and July (1.52 \u00b0C), and persisting until summer 2023 with anomalies around 1 \u00b0C above the 1991-2020 climatology. The peak of July 2023 (1.76 \u00b0C) set the record of highest SST anomaly ever recorded since 1982. The 2022/2023 Mediterranean marine heatwave is comparable to that occurred in 2003 (see e.g. Olita et al., 2007) in terms of anomaly magnitude but longer in duration.\nOver the period 1982-2023, the Mediterranean SST has warmed at a rate of 0.041 \u00b1 0.001 \u00b0C/year, which corresponds to an average increase of about 1.7 \u00b0C during these last 42 years. Within its error (namely, the 95% confidence interval), this warming trend is consistent with recent trend estimates in the Mediterranean Sea (Pisano et al., 2020; Pastor et al., 2020). However, though the linear trend being constantly increasing during the whole period, the picture of the Mediterranean SST trend in 2022 seems to reveal a restarting after the pause occurred in the last years (since 2015-2021).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00268\n\n**References:**\n\n* Giorgi, F., 2006. Climate change hot-spots. Geophys. Res. Lett., 33:L08707, https://doi.org/10.1029/2006GL025734\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. doi:10.1080/1755876X.2016.1273446.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Pisano, A., Marullo, S., Artale, V., Falcini, F., Yang, C., Leonelli, F. E., Santoleri, R. and Buongiorno Nardelli, B.: New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations, Remote Sens., 12(1), 132, doi:10.3390/rs12010132, 2020.\n* Pastor, F., Valiente, J. A., & Khodayar, S. (2020). A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature. Remote Sensing, 12(17), 2687.\n* Olita, A., Sorgente, R., Natale, S., Gaber\u0161ek, S., Ribotti, A., Bonanno, A., & Patti, B. (2007). Effects of the 2003 European heatwave on the Central Mediterranean Sea: surface fluxes and the dynamical response. Ocean Science, 3(2), 273-289.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00268", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-tempsal-sst-area-averaged-anomalies,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Surface Temperature time series and trend from Observations Reprocessing"}, "MEDSEA_OMI_TEMPSAL_sst_trend": {"abstract": "**DEFINITION**\n\nThe medsea_omi_tempsal_sst_trend product includes the cumulative/net Sea Surface Temperature (SST) trend for the Mediterranean Sea over the period 1982-2023, i.e. the rate of change (\u00b0C/year) multiplied by the number years in the time series (42 years). This OMI is derived from the CMEMS Reprocessed Mediterranean L4 SST product (SST_MED_SST_L4_REP_OBSERVATIONS_010_021, see also the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf), which provides the SSTs used to compute the SST trend over the Mediterranean Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05\u00b0 grid resolution SST maps over the Mediterranean Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005; Pisano et al., 2020), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterize the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). The Mediterranean Sea is a climate change hotspot (Giorgi F., 2006). Indeed, Mediterranean SST has experienced a continuous warming trend since the beginning of 1980s (e.g., Pisano et al., 2020; Pastor et al., 2020). Specifically, since the beginning of the 21st century (from 2000 onward), the Mediterranean Sea featured the highest SSTs and this warming trend is expected to continue throughout the 21st century (Kirtman et al., 2013). \n\n**KEY FINDINGS**\n\nOver the past four decades (1982-2023), the Mediterranean Sea surface temperature (SST) warmed at a rate of 0.041 \u00b1 0.001 \u00b0C per year, corresponding to a mean surface temperature warming of about 1.7 \u00b0C. The spatial pattern of the Mediterranean SST trend shows a general warming tendency, ranging from 0.002 \u00b0C/year to 0.063 \u00b0C/year. Overall, a higher SST trend intensity characterizes the Eastern and Central Mediterranean basin with respect to the Western basin. In particular, the Balearic Sea, Tyrrhenian and Adriatic Seas, as well as the northern Ionian and Aegean-Levantine Seas show the highest SST trends (from 0.04 \u00b0C/year to 0.05 \u00b0C/year on average). Trend patterns of warmer intensity characterize some of main sub-basin Mediterranean features, such as the Pelops Anticyclone, the Cretan gyre and the Rhodes Gyre. On the contrary, less intense values characterize the southern Mediterranean Sea (toward the African coast), where the trend attains around 0.025 \u00b0C/year. The SST warming rate spatial change, mostly showing an eastward increase pattern (see, e.g., Pisano et al., 2020, and references therein), i.e. the Levantine basin getting warm faster than the Western, appears now to have tilted more along a North-South direction.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00269\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Giorgi, F., 2006. Climate change hot-spots. Geophys. Res. Lett., 33:L08707, https://doi.org/10.1029/2006GL025734 Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Kirtman, B., Power, S. B, Adedoyin, J. A., Boer, G. J., Bojariu, R. et al., 2013. Near-term climate change: Projections and Predictability. In: Stocker, T.F., et al. (Eds.), Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Mulet, S., Buongiorno Nardelli, B., Good, S., Pisano, A., Greiner, E., Monier, M., Autret, E., Axell, L., Boberg, F., Ciliberti, S., Dr\u00e9villon, M., Droghei, R., Embury, O., Gourrion, J., H\u00f8yer, J., Juza, M., Kennedy, J., Lemieux-Dudon, B., Peneva, E., Reid, R., Simoncelli, S., Storto, A., Tinker, J., Von Schuckmann, K., Wakelin, S. L., 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s5\u2013s13, DOI: 10.1080/1755876X.2018.1489208\n* Pastor, F., Valiente, J. A., & Khodayar, S. (2020). A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature. Remote Sensing, 12(17), 2687.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Pisano, A., Marullo, S., Artale, V., Falcini, F., Yang, C., Leonelli, F. E., Santoleri, R. and Buongiorno Nardelli, B.: New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations, Remote Sens., 12(1), 132, doi:10.3390/rs12010132, 2020.\n* Roquet, H., Pisano, A., Embury, O., 2016. Sea surface temperature. In: von Schuckmann et al. 2016, The Copernicus Marine Environment Monitoring Service Ocean State Report, Jour. Operational Ocean., vol. 9, suppl. 2. doi:10.1080/1755876X.2016.1273446.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00269", "instrument": null, "keywords": "change-over-time-in-sea-surface-temperature,coastal-marine-environment,marine-resources,marine-safety,mediterranean-sea,medsea-omi-tempsal-sst-trend,multi-year,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009": {"abstract": "This product consists of vertical profiles of the concentration of nutrients (nitrates, phosphates, and silicates) and carbonate system variables (total alkalinity, dissolved inorganic carbon, pH, and partial pressure of carbon dioxide), computed for each Argo float equipped with an oxygen sensor.\nThe method called CANYON (Carbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) is based on a neural network trained using high-quality nutrient data collected over the last 30 years (GLODAPv2 database, https://www.glodap.info/). The method is applied to each Argo float equipped with an oxygen sensor using as input the properties measured by the float (pressure, temperature, salinity, oxygen), and its date and position.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00048\n\n**References:**\n\n* Sauzede R., H. C. Bittig, H. Claustre, O. Pasqueron de Fommervault, J.-P. Gattuso, L. Legendre and K. S. Johnson, 2017: Estimates of Water-Column Nutrient Concentrations and Carbonate System Parameters in the Global Ocean: A novel Approach Based on Neural Networks. Front. Mar. Sci. 4:128. doi: 10.3389/fmars.2017.00128.\n* Bittig H. C., T. Steinhoff, H. Claustre, B. Fiedler, N. L. Williams, R. Sauz\u00e8de, A. K\u00f6rtzinger and J.-P. Gattuso,2018: An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks. Front. Mar. Sci. 5:328. doi: 10.3389/fmars.2018.00328.\n", "doi": "10.48670/moi-00048", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,moles-of-nitrate-per-unit-mass-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,moles-of-phosphate-per-unit-mass-in-sea-water,moles-of-silicate-per-unit-mass-in-sea-water,multi-year,multiobs-glo-bgc-nutrients-carbon-profiles-mynrt-015-009,none,oceanographic-geographical-features,partial-pressure-of-carbon-dioxide-in-sea-water,sea-water-ph-reported-on-total-scale,sea-water-pressure,sea-water-salinity,sea-water-temperature,total-alkalinity-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nutrient and carbon profiles vertical distribution"}, "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010": {"abstract": "This product consists of 3D fields of Particulate Organic Carbon (POC), Particulate Backscattering coefficient (bbp) and Chlorophyll-a concentration (Chla) at depth. The reprocessed product is provided at 0.25\u00b0x0.25\u00b0 horizontal resolution, over 36 levels from the surface to 1000 m depth. \nA neural network method estimates both the vertical distribution of Chla concentration and of particulate backscattering coefficient (bbp), a bio-optical proxy for POC, from merged surface ocean color satellite measurements with hydrological properties and additional relevant drivers. \n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00046\n\n**References:**\n\n* Sauzede R., H. Claustre, J. Uitz, C. Jamet, G. Dall\u2019Olmo, F. D\u2019Ortenzio, B. Gentili, A. Poteau, and C. Schmechtig, 2016: A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient, J. Geophys. Res. Oceans, 121, doi:10.1002/2015JC011408.\n", "doi": "10.48670/moi-00046", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,multi-year,multiobs-glo-bio-bgc-3d-rep-015-010,none,oceanographic-geographical-features,satellite-observation,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1998-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008": {"abstract": "This product corresponds to a REP L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 0.25\u00b0 x 0.25\u00b0 regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al. 2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00047\n\n**References:**\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Chau, T.-T.-T., Chevallier, F., & Gehlen, M. (2024). Global analysis of surface ocean CO2 fugacity and air-sea fluxes with low latency. Geophysical Research Letters, 51, e2023GL106670. https://doi.org/10.1029/2023GL106670\n* Chau, T.-T.-T., Gehlen, M., Metzl, N., and Chevallier, F.: CMEMS-LSCE: a global, 0.25\u00b0, monthly reconstruction of the surface ocean carbonate system, Earth Syst. Sci. Data, 16, 121\u2013160, https://doi.org/10.5194/essd-16-121-2024, 2024.\n", "doi": "10.48670/moi-00047", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-bio-carbon-surface-mynrt-015-008,none,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,total-alkalinity-in-sea-water,uncertainty-surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,uncertainty-surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Surface ocean carbon fields"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008": {"abstract": "This product corresponds to a REP L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 0.25\u00b0 x 0.25\u00b0 regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al. 2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\n**Product Citation**: Please refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00047\n\n**References:**\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n", "doi": "10.48670/moi-00047", "instrument": null, "keywords": "coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-bio-carbon-surface-rep-015-008,none,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,total-alkalinity-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "LSCE (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Surface Carbon"}, "MULTIOBS_GLO_PHY_MYNRT_015_003": {"abstract": "This product is a L4 REP and NRT global total velocity field at 0m and 15m together wiht its individual components (geostrophy and Ekman) and related uncertainties. It consists of the zonal and meridional velocity at a 1h frequency and at 1/4 degree regular grid. The total velocity fields are obtained by combining CMEMS satellite Geostrophic surface currents and modelled Ekman currents at the surface and 15m depth (using ERA5 wind stress in REP and ERA5* in NRT). 1 hourly product, daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program).\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00327\n\n**References:**\n\n* Rio, M.-H., S. Mulet, and N. Picot: Beyond GOCE for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new insight into geostrophic and Ekman currents, Geophys. Res. Lett., 41, doi:10.1002/2014GL061773, 2014.\n", "doi": "10.48670/mds-00327", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-mynrt-015-003,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,numerical-model,oceanographic-geographical-features,satellite-observation,sea-water-x-velocity-due-to-tide,sea-water-y-velocity-due-to-tide,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014": {"abstract": "The product MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014 is a reformatting and a simplified version of the CATDS L3 product called \u201c2Q\u201d or \u201cL2Q\u201d. it is an intermediate product, that provides, in daily files, SSS corrected from land-sea contamination and latitudinal bias, with/without rain freshening correction.\n\n**DOI (product):** \nhttps://doi.org/10.1016/j.rse.2016.02.061\n\n**References:**\n\n* Boutin, J., J. L. Vergely, S. Marchand, F. D'Amico, A. Hasson, N. Kolodziejczyk, N. Reul, G. Reverdin, and J. Vialard (2018), New SMOS Sea Surface Salinity with reduced systematic errors and improved variability, Remote Sensing of Environment, 214, 115-134. doi:https://doi.org/10.1016/j.rse.2018.05.022\n* Kolodziejczyk, N., J. Boutin, J.-L. Vergely, S. Marchand, N. Martin, and G. Reverdin (2016), Mitigation of systematic errors in SMOS sea surface salinity, Remote Sensing of Environment, 180, 164-177. doi:https://doi.org/10.1016/j.rse.2016.02.061\n", "doi": "10.1016/j.rse.2016.02.061", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,multi-year,multiobs-glo-phy-sss-l3-mynrt-015-014,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-salinity,sea-surface-salinity-error,sea-surface-salinity-qc,sea-surface-salinity-rain-corrected-error,sea-surface-salinity-sain-corrected,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2010-01-12T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015": {"abstract": "The product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called \u201cSMOS-OI\u201d. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information.\n\nKolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version: https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version: https://archimer.ifremer.fr/doc/00665/77702/\n\n**DOI (product):** \nhttps://doi.org/10.1175/JTECH-D-20-0093.1\n\n**References:**\n\n* Kolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/\n", "doi": "10.1175/JTECH-D-20-0093.1", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-sss-l4-my-015-015,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,sea-surface-temperature,sea-water-conservative-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2010-06-03T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "SSS SMOS/SMAP L4 OI - LOPS-v2023"}, "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013": {"abstract": "This product consits of daily global gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/8\u00b0 of resolution, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA\u2019s Soil Moisture Active Passive (SMAP) and ESA\u2019s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite SST information. The product was developed by the Consiglio Nazionale delle Ricerche (CNR) and includes 4 datasets:\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1D, which provides near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1M, which provides near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy-sss_my_multi_P1D, which provides multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy-sss_my_multi_P1M, which provides multi-year reprocessed (REP) monthly data \n\n**Product citation**: \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00051\n\n**References:**\n\n* Droghei, R., B. Buongiorno Nardelli, and R. Santoleri, 2016: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface. J. Atmos. Oceanic Technol. doi:10.1175/JTECH-D-15-0194.1.\n* Buongiorno Nardelli, B., R. Droghei, and R. Santoleri, 2016: Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data. Rem. Sens. Environ., doi:10.1016/j.rse.2015.12.052.\n* Droghei, R., B. Buongiorno Nardelli, and R. Santoleri, 2018: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993\u20132016), Front. Mar. Sci., 5(March), 1\u201313, doi:10.3389/fmars.2018.00084.\n* Sammartino, Michela, Salvatore Aronica, Rosalia Santoleri, and Bruno Buongiorno Nardelli. (2022). Retrieving Mediterranean Sea Surface Salinity Distribution and Interannual Trends from Multi-Sensor Satellite and In Situ Data, Remote Sensing 14, 2502: https://doi.org/10.3390/rs14102502.\n", "doi": "10.48670/moi-00051", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-s-surface-mynrt-015-013,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012": {"abstract": "You can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing. It consists of 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/8 degree regular grid and on 50 depth levels from the surface down to the bottom. The product includes 5 datasets: \n* cmems_obs-mob_glo_phy_nrt_0.125deg_P1D-m, which delivers near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy_nrt_0.125deg_P1M-m, which delivers near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy_my_0.125deg_P1D-m, which delivers multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy_my_0.125deg_P1M-m, which delivers multi-year reprocessed (REP) monthly data\n* cmems_obs-mob_glo_phy_mynrt_0.125deg-climatology-uncertainty_P1M-m, which delivers monthly static uncertainty data\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00052\n\n**References:**\n\n* Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845\u2013857.\n* Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77\u201380(0):70\u201381.\n", "doi": "10.48670/moi-00052", "instrument": null, "keywords": "coastal-marine-environment,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-tsuv-3d-mynrt-015-012,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "MULTIOBS_GLO_PHY_W_3D_REP_015_007": {"abstract": "You can find here the OMEGA3D observation-based quasi-geostrophic vertical and horizontal ocean currents developed by the Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4\u00b0 horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_REP_015_002 which corresponds to former version of MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012) and ERA-Interim surface fluxes. \n\n**DOI (product):** \nhttps://doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007\n\n**References:**\n\n* Buongiorno Nardelli, B. A Multi-Year Timeseries of Observation-Based 3D Horizontal and Vertical Quasi-Geostrophic Global Ocean Currents. Earth Syst. Sci. Data 2020, No. 12, 1711\u20131723. https://doi.org/10.5194/essd-12-1711-2020.\n", "doi": "10.25423/cmcc/multiobs_glo_phy_w_rep_015_007", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-w-3d-rep-015-007,northward-sea-water-velocity,numerical-model,oceanographic-geographical-features,satellite-observation,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-06T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D)"}, "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"abstract": "**DEFINITION**\n\nThe CMEMS NORTHWESTSHELF_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The North-West Shelf Multi Year Product (NWSHELF_MULTIYEAR_PHY_004_009) and the Analysis product (NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013).\nTwo parameters are included on this OMI:\n* Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019).\n* Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile.\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).\n\n**CONTEXT**\n\nThis domain comprises the North West European continental shelf where depths do not exceed 200m and deeper Atlantic waters to the North and West. For these deeper waters, the North-South temperature gradient dominates (Liu and Tanhua, 2021). Temperature over the continental shelf is affected also by the various local currents in this region and by the shallow depth of the water (Elliott et al., 1990). Atmospheric heat waves can warm the whole water column, especially in the southern North Sea, much of which is no more than 30m deep (Holt et al., 2012). Warm summertime water observed in the Norwegian trench is outflow heading North from the Baltic Sea and from the North Sea itself.\n\n**CMEMS KEY FINDINGS**\n\nThe 99th percentile SST product can be considered to represent approximately the warmest 4 days for the sea surface in Summer. Maximum anomalies for 2020 are up to 4oC warmer than the 1993-2019 average in the western approaches, Celtic and Irish Seas, English Channel and the southern North Sea. For the atmosphere, Summer 2020 was exceptionally warm and sunny in southern UK (Kendon et al., 2021), with heatwaves in June and August. Further north in the UK, the atmosphere was closer to long-term average temperatures. Overall, the 99th percentile SST anomalies show a similar pattern, with the exceptional warm anomalies in the south of the domain.\n\nNote: The key findings will be updated annually in November, in line with OMI evolutions.\n\n**DOI (product)**\nhttps://doi.org/10.48670/moi-00273\n\n**References:**\n\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al. 2019. Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Elliott, A.J., Clarke, T., Li, ., 1990: Monthly distributions of surface and bottom temperatures in the northwest European shelf seas. Continental Shelf Research, Vol 11, no 5, pp 453-466, http://doi.org/10.1016/0278-4343(91)90053-9\n* Holt, J., Hughes, S., Hopkins, J., Wakelin, S., Holliday, P.N., Dye, S., Gonz\u00e1lez-Pola, C., Hj\u00f8llo, S., Mork, K., Nolan, G., Proctor, R., Read, J., Shammon, T., Sherwin, T., Smyth, T., Tattersall, G., Ward, B., Wiltshire, K., 2012: Multi-decadal variability and trends in the temperature of the northwest European continental shelf: A model-data synthesis. Progress in Oceanography, 96-117, 106, http://doi.org/10.1016/j.pocean.2012.08.001\n* Kendon, M., McCarthy, M., Jevrejeva, S., Matthews, A., Sparks, T. and Garforth, J. (2021), State of the UK Climate 2020. Int J Climatol, 41 (Suppl 2): 1-76. https://doi.org/10.1002/joc.7285\n* Liu, M., Tanhua, T., 2021: Water masses in the Atlantic Ocean: characteristics and distributions. Ocean Sci, 17, 463-486, http://doi.org/10.5194/os-17-463-2021\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n", "doi": "10.48670/moi-00273", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northwestshelf-omi-tempsal-extreme-var-temp-mean-and-anomaly,numerical-model,oceanographic-geographical-features,temp-percentile99-anom,temp-percentile99-mean,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf Sea Surface Temperature extreme from Reanalysis"}, "NWSHELF_ANALYSISFORECAST_BGC_004_002": {"abstract": "The NWSHELF_ANALYSISFORECAST_BGC_004_002 is produced by a coupled physical-biogeochemical model, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated weekly, providing 10-day forecast of the main biogeochemical variables.\nProducts are provided as daily and monthly means.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00056", "doi": "10.48670/moi-00056", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,nwshelf-analysisforecast-bgc-004-002,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-05-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "NWSHELF_ANALYSISFORECAST_PHY_004_013": {"abstract": "The NWSHELF_ANALYSISFORECAST_PHY_004_013 is produced by a hydrodynamic model with tides, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated daily, providing 10-day forecast for temperature, salinity, currents, sea level and mixed layer depth.\nProducts are provided at quarter-hourly, hourly, daily de-tided (with Doodson filter), and monthly frequency.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00054", "doi": "10.48670/moi-00054", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,nwshelf-analysisforecast-phy-004-013,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tide,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "NWSHELF_ANALYSISFORECAST_WAV_004_014": {"abstract": "The NWSHELF_ANALYSISFORECAST_WAV_004_014 is produced by a wave model system based on MFWAV, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution forced by ECMWF wind data. The system assimilates significant wave height altimeter data and spectral data, and it is forced by currents provided by the [ ref t the physical system] ocean circulation system.\nThe product is updated twice a day, providing 10-day forecast of wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state, and wind-state, and swell components. \nProducts are provided at hourly frequency\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00055\n\n**References:**\n\n* The impact of ocean-wave coupling on the upper ocean circulation during storm events (Bruciaferri, D., Tonani, M., Lewis, H., Siddorn, J., Saulter, A., Castillo, J.M., Garcia Valiente, N., Conley, D., Sykes, P., Ascione, I., McConnell, N.) in Journal of Geophysical Research, Oceans, 2021, 126, 6. https://doi.org/10.1029/2021JC017343\n", "doi": "10.48670/moi-00055", "instrument": null, "keywords": "coastal-marine-environment,forecast,level-4,marine-resources,marine-safety,near-real-time,none,north-west-shelf-seas,numerical-model,nwshelf-analysisforecast-wav-004-014,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-10-06T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast"}, "NWSHELF_MULTIYEAR_BGC_004_011": {"abstract": "**Short Description:**\n\nThe ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butenschön et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009). Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data.\n\nThe description of the model and its configuration, including the products validation is provided in the [CMEMS-NWS-QUID-004-011](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-011.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually, providing a six-month extension of the time series. See [CMEMS-NWS-PUM-004-009_011](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf) for details.\n\n**Associated products:**\n\nThis model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [NWSHELF_MULTIYEAR_PHY_004_009](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009).\nAn analysis-forecast product is available from: [NWSHELF_MULTIYEAR_BGC_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00058\n\n**References:**\n\n* Ciavatta, S., Brewin, R. J. W., Sk\u00e1kala, J., Polimene, L., de Mora, L., Artioli, Y., & Allen, J. I. (2018). [https://doi.org/10.1002/2017JC013490 Assimilation of ocean\u2010color plankton functional types to improve marine ecosystem simulations]. Journal of Geophysical Research: Oceans, 123, 834\u2013854. https://doi.org/10.1002/2017JC013490\n", "doi": "10.48670/moi-00058", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,nwshelf-multiyear-bgc-004-011,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "NWSHELF_MULTIYEAR_PHY_004_009": {"abstract": "**Short Description:**\n\nThe ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution. \nThe ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [GLOBAL_REANALYSIS_PHY_001_026](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026) and at the Baltic boundary by the [BALTICSEA_REANALYSIS_PHY_003_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_PHY_003_011). The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. \n\nFurther details of the model, including the product validation are provided in the [CMEMS-NWS-QUID-004-009](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-009.pdf). \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually provinding six-month extension of the time series.\n\nSee [CMEMS-NWS-PUM-004-009_011](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf) for further details.\n\n**Associated products:**\n\nThis model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011). An analysis-forecast product is available from [NWSHELF_ANALYSISFORECAST_PHY_LR_004_011](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001).\nThe product is updated biannually provinding six-month extension of the time series.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00059", "doi": "10.48670/moi-00059", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,nwshelf-multiyear-phy-004-009,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "NWSHELF_REANALYSIS_WAV_004_015": {"abstract": "**Short description:**\n\nThis product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013). The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [ECMWF ERA-5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [CMEMS-NWS-PUM-004-015](https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-015.pdf) for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [CMEMS-NWS-QUID-004-015](https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-015.pdf).\nThe product is updated biannually provinding six-month extension of the time series.\n\n**Associated products:**\n\n[NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014](https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00060", "doi": "10.48670/moi-00060", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,none,north-west-shelf-seas,numerical-model,nwshelf-reanalysis-wav-004-015,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "Met Office (UK)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic- European North West Shelf- Wave Physics Reanalysis"}, "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products of region ARC are delivered in polar Lambertian Azimuthal Equal Area (LAEA) projection (EPSG:6931, EASE2). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-m\n*cmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-m\n*cmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-m\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00061\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00061", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-arc-bgc-hr-l3-nrt-009-201,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Region, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products of region ARC are delivered in polar Lambertian Azimuthal Equal Area (LAEA) projection (EPSG:6931, EASE2). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n\n**Dataset names: **\n*cmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00062\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00062", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-arc-bgc-hr-l4-nrt-009-207,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_ARC_BGC_L3_MY_009_123": {"abstract": "For the **Arctic** Ocean **Satellite Observations**, Italian National Research Council (CNR \u2013 Rome, Italy) is providing **Bio-Geo_Chemical (BGC)** products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the **\"multi\"** products and S3A & S3B only for the **\"OLCI\"** products.\n* Variables: Chlorophyll-a (**CHL**), Diffuse Attenuation (**KD490**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **4 km** (multi) or **300 m** (OLCI).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00292", "doi": "10.48670/moi-00292", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-arc-bgc-l3-my-009-123,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121": {"abstract": "For the **Arctic** Ocean **Satellite Observations**, Italian National Research Council (CNR \u2013 Rome, Italy) is providing **Bio-Geo_Chemical (BGC)** products.\n* Upstreams: OLCI-S3A & OLCI-S3B for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Suspended Matter (**SPM**), Diffuse Attenuation (**KD490**), Detrital and Dissolved Material Absorption Coef. (**ADG443_), Phytoplankton Absorption Coef. (**APH443_), Total Absorption Coef. (**ATOT443_) and Reflectance (**RRS_').\n\n* Temporal resolutions: **daily**, **monthly**.\n* Spatial resolutions: **300 meters** (olci).\n* Recent products are organized in datasets called Near Real Time (**NRT**).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00290", "doi": "10.48670/moi-00290", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-arc-bgc-l3-nrt-009-121,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_ARC_BGC_L4_MY_009_124": {"abstract": "For the **Arctic** Ocean **Satellite Observations**, Italian National Research Council (CNR \u2013 Rome, Italy) is providing **Bio-Geo_Chemical (BGC)** products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the **\"multi\"** products , and S3A & S3B only for the **\"OLCI\"** products.\n* Variables: Chlorophyll-a (**CHL**), Diffuse Attenuation (**KD490**)\n\n\n* Temporal resolutions: **monthly**.\n* Spatial resolutions: **4 km** (multi) or **300 meters** (OLCI).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00293", "doi": "10.48670/moi-00293", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-arc-bgc-l4-my-009-124,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton MY L4 daily climatology and monthly observations"}, "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122": {"abstract": "For the **Arctic** Ocean **Satellite Observations**, Italian National Research Council (CNR \u2013 Rome, Italy) is providing **Bio-Geo_Chemical (BGC)** products.\n* Upstreams: OLCI-S3A & OLCI-S3B for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**) and Diffuse Attenuation (**KD490**).\n\n* Temporal resolutions:**monthly**.\n* Spatial resolutions: **300 meters** (olci).\n* Recent products are organized in datasets called Near Real Time (**NRT**).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00291", "doi": "10.48670/moi-00291", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-arc-bgc-l4-nrt-009-122,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Colour Plankton and Transparency L4 NRT monthly observations"}, "OCEANCOLOUR_ATL_BGC_L3_MY_009_113": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **1 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00286", "doi": "10.48670/moi-00286", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-atl-bgc-l3-my-009-113,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **1 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00284", "doi": "10.48670/moi-00284", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-atl-bgc-l3-nrt-009-111,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-21T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_ATL_BGC_L4_MY_009_118": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and S3A & S3B only for the **\"olci\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: **1 km**.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"GlobColour\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00289", "doi": "10.48670/moi-00289", "instrument": null, "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-atl-bgc-l4-my-009-118,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and S3A & S3B only for the **\"olci\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: **1 km**.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"GlobColour\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00288", "doi": "10.48670/moi-00288", "instrument": null, "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-atl-bgc-l4-nrt-009-116,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-27T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_bal_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l3-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00079\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00079", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-bal-bgc-hr-l3-nrt-009-202,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00080\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00080", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-bal-bgc-hr-l4-nrt-009-208,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-08T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_BAL_BGC_L3_MY_009_133": {"abstract": "For the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv6) for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolution**: 1 km for **\"\"multi\"\"** (4 km for **\"\"pp\"\"**) and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BAL_BGC_L3_MY\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00296\n\n**References:**\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "doi": "10.48670/moi-00296", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-bal-bgc-l3-my-009-133,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131": {"abstract": "For the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n**Upstreams**: OLCI-S3A & S3B \n\n**Temporal resolution**: daily\n\n**Spatial resolution**: 300 meters \n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BAL_BGC_L3_NRT\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00294\n\n**References:**\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic Sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n", "doi": "10.48670/moi-00294", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-bal-bgc-l3-nrt-009-131,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-18T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "OCEANCOLOUR_BAL_BGC_L4_MY_009_134": {"abstract": "For the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolutions**: monthly\n\n**Spatial resolution**: 1 km for **\"\"multi\"\"** and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BAL_BGC_L4_MY\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00308\n\n**References:**\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "doi": "10.48670/moi-00308", "instrument": null, "keywords": "baltic-sea,chl,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-bal-bgc-l4-my-009-134,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132": {"abstract": "For the **Baltic Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n**Upstreams**: OLCI-S3A & S3B \n\n**Temporal resolution**: monthly \n\n**Spatial resolution**: 300 meters \n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BAL_BGC_L4_NRT\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00295\n\n**References:**\n\n* Brando, V. E., Sammartino, M., Colella, S., Bracaglia, M., Di Cicco, A., D\u2019Alimonte, D., ... & Attila, J. (2021). Phytoplankton bloom dynamics in the Baltic Sea using a consistently reprocessed time series of multi-sensor reflectance and novel chlorophyll-a retrievals. Remote Sensing, 13(16), 3071\n", "doi": "10.48670/moi-00295", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-bal-bgc-l4-nrt-009-132,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations"}, "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided within 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_blk_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l3-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00086\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00086", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-blk-bgc-hr-l3-nrt-009-206,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00087\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00087", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-blk-bgc-hr-l4-nrt-009-212,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-02T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_BLK_BGC_L3_MY_009_153": {"abstract": "For the **Black Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and OLCI-S3A & S3B for the **\"olci\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolution**: 1 km for **\"multi\"** and 300 meters for **\"olci\"**\n\nTo find this product in the catalogue, use the search keyword **\"OCEANCOLOUR_BLK_BGC_L3_MY\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00303\n\n**References:**\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00303", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-blk-bgc-l3-my-009-153,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-16T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151": {"abstract": "For the **Black Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolutions**: 1 km for **\"\"multi\"\"** and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BLK_BGC_L3_NRT\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00301\n\n**References:**\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00301", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-blk-bgc-l3-nrt-009-151,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-29T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_BLK_BGC_L4_MY_009_154": {"abstract": "For the **Black Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated **gap-free** Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolutions**: monthly and daily (for **\"\"gap-free\"\"**, **\"\"pp\"\"** and climatology data)\n\n**Spatial resolution**: 1 km for **\"\"multi\"\"** (4 km for **\"\"pp\"\"**) and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BLK_BGC_L4_MY\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00304\n\n**References:**\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00304", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-blk-bgc-l4-my-009-154,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152": {"abstract": "For the **Black Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated **gap-free** Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for **\"\"multi**\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolutions**: monthly and daily (for **\"\"gap-free\"\"** and **\"\"pp\"\"** data)\n\n**Spatial resolutions**: 1 km for **\"\"multi\"\"** (4 km for **\"\"pp\"\"**) and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_BLK_BGC_L4_NRT\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00302\n\n**References:**\n\n* Kajiyama T., D. D\u2019Alimonte, and G. Zibordi, \u201cAlgorithms merging for the determination of Chlorophyll-a concentration in the Black Sea,\u201d IEEE Geoscience and Remote Sensing Letters, 2018. [Online]. Available: https://-www.doi.org/\u00ac10.1+D7109/\u00acLGRS.2018.2883539\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Zibordi, G., F. Me\u0301lin, J.-F. Berthon, and M. Talone (2015). In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea. Ocean Sci., 11, 275\u2013286.\n", "doi": "10.48670/moi-00302", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,oceancolour-blk-bgc-l4-nrt-009-152,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_103": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\"\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00280", "doi": "10.48670/moi-00280", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-glo-bgc-l3-my-009-103,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_107": {"abstract": "For the **Global** Ocean **Satellite Observations**, Brockmann Consult (BC) is providing **Bio-Geo_Chemical (BGC)** products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the **\"\"multi\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**, **monthly**.\n* Spatial resolutions: **4 km** (multi).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find these products in the catalogue, use the search keyword **\"\"ESA-CCI\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00282", "doi": "10.48670/moi-00282", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-glo-bgc-l3-my-009-107,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "BC (Germany)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00278", "doi": "10.48670/moi-00278", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-glo-bgc-l3-nrt-009-101,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_104": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"\"cloud free\"\" product.\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\"\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00281", "doi": "10.48670/moi-00281", "instrument": null, "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,oceancolour-glo-bgc-l4-my-009-104,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_108": {"abstract": "For the **Global** Ocean **Satellite Observations**, Brockmann Consult (BC) is providing **Bio-Geo_Chemical (BGC)** products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the **\"\"multi\"\"** products.\n* Variables: Chlorophyll-a (**CHL**).\n\n* Temporal resolutions: **monthly**.\n* Spatial resolutions: **4 km** (multi).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find these products in the catalogue, use the search keyword **\"\"ESA-CCI\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00283", "doi": "10.48670/moi-00283", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,oceancolour-glo-bgc-l4-my-009-108,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour Plankton MY L4 monthly observations"}, "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102": {"abstract": "For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and S3A & S3B only for the **\"olci\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"GlobColour\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00279", "doi": "10.48670/moi-00279", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-glo-bgc-l4-nrt-009-102,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": "proprietary", "missionStartDate": "2023-04-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_nws_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l3-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00107\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00107", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-ibi-bgc-hr-l3-nrt-009-204,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberic Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00108\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00108", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-ibi-bgc-hr-l4-nrt-009-210,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_ibi_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l3-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00109\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00109", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-hr-l3-nrt-009-205,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1-) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1M-v01+D19\n*cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00110\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00110", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-hr-l4-nrt-009-211,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OCEANCOLOUR_MED_BGC_L3_MY_009_143": {"abstract": "For the **Mediterranean Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for **\"multi**\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n* **_pp**_ with the Integrated Primary Production (PP)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and OLCI-S3A & S3B for the **\"olci\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolution**: 1 km for **\"multi\"** and 300 meters for **\"olci\"**\n\nTo find this product in the catalogue, use the search keyword **\"OCEANCOLOUR_MED_BGC_L3_MY\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00299\n\n**References:**\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Di Cicco A, Sammartino M, Marullo S and Santoleri R (2017) Regional Empirical Algorithms for an Improved Identification of Phytoplankton Functional Types and Size Classes in the Mediterranean Sea Using Satellite Data. Front. Mar. Sci. 4:126. doi: 10.3389/fmars.2017.00126\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00299", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,oceancolour-med-bgc-l3-my-009-143,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-16T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_MED_BGC_L3_NRT_009_141": {"abstract": "For the **Mediterranean Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* **_reflectance**_ with the spectral Remote Sensing Reflectance (RRS)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for **\"\"multi**\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* **_optics**_ including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolution**: daily\n\n**Spatial resolutions**: 1 km for **\"\"multi\"\"** and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_MED_BGC_L3_NRT\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00297\n\n**References:**\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Di Cicco A, Sammartino M, Marullo S and Santoleri R (2017) Regional Empirical Algorithms for an Improved Identification of Phytoplankton Functional Types and Size Classes in the Mediterranean Sea Using Satellite Data. Front. Mar. Sci. 4:126. doi: 10.3389/fmars.2017.00126\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00297", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-l3-nrt-009-141,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2023-04-29T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_MED_BGC_L4_MY_009_144": {"abstract": "For the **Mediterranean Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated **gap-free** Chl concentration (to provide a \"cloud free\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"multi\"** products, and OLCI-S3A & S3B for the **\"olci\"** products\n\n**Temporal resolutions**: monthly and daily (for **\"gap-free\"** and climatology data)\n\n**Spatial resolution**: 1 km for **\"multi\"** and 300 meters for **\"olci\"**\n\nTo find this product in the catalogue, use the search keyword **\"OCEANCOLOUR_MED_BGC_L4_MY\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00300\n\n**References:**\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00300", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,oceancolour-med-bgc-l4-my-009-144,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "OCEANCOLOUR_MED_BGC_L4_NRT_009_142": {"abstract": "For the **Mediterranean Sea** Ocean **Satellite Observations**, the Italian National Research Council (CNR \u2013 Rome, Italy), is providing **Bio-Geo_Chemical (BGC)** regional datasets:\n* **_plankton**_ with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated **gap-free** Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* **_transparency**_ with the diffuse attenuation coefficient of light at 490 nm (KD490) (for **\"\"multi**\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* **_pp**_ with the Integrated Primary Production (PP).\n\n**Upstreams**: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and OLCI-S3A & S3B for the **\"\"olci\"\"** products\n\n**Temporal resolutions**: monthly and daily (for **\"\"gap-free\"\"** and **\"\"pp\"\"** data)\n\n**Spatial resolutions**: 1 km for **\"\"multi\"\"** (4 km for **\"\"pp\"\"**) and 300 meters for **\"\"olci\"\"**\n\nTo find this product in the catalogue, use the search keyword **\"\"OCEANCOLOUR_MED_BGC_L4_NRT\"\"**.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00298\n\n**References:**\n\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004\n* Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintor\u00e8, and J. Verron, pp. 227\u2013244\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00298", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,oceancolour-med-bgc-l4-nrt-009-142,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n\n*cmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00118\n\n**References:**\n\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00118", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,oceancolour-nws-bgc-hr-l3-nrt-009-203,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf Region, Bio-Geo-Chemical, L3, daily observation"}, "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209": {"abstract": "The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n**Processing information:**\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n**Description of observation methods/instruments:**\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n**Quality / Accuracy / Calibration information:**\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n**Suitability, Expected type of users / uses:**\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n**Dataset names: **\n*cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1D-v01\n\n**Files format:**\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00119\n\n**References:**\n\n* Alvera-Azc\u00e1rate, Aida, et al. (2021), Detection of shadows in high spatial resolution ocean satellite data using DINEOF. Remote Sensing of Environment 253: 112229.\n* Lavigne, H., et al. (2021), Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters, Remote Sensing of Environment, in press.\n* Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772.\n* Novoa, S., et al. (2017), Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sens., v. 9, 61.\n* Gons, et al. (2005), Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., v. 27, n. 1, p. 125-127.\n* O'Reilly, et al. (2019), Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment. 229, 32\u201347.\n", "doi": "10.48670/moi-00119", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,north-west-shelf-seas,oceancolour-nws-bgc-hr-l4-nrt-009-209,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-04T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index": {"abstract": "**DEFINITION**\n\nThe Black Sea Rim Current index (BSRCI) reflects the intensity of the Rim current, which is a main feature of the Black Sea circulation, a basin scale cyclonic current. The index was computed using sea surface current speed averaged over two areas of intense currents based on reanalysis data. The areas are confined between the 200 and 1800 m isobaths in the northern section 33-39E (from the Caucasus coast to the Crimea Peninsula), and in the southern section 31.5-35E (from Sakarya region to near Sinop Peninsula). Thus, three indices were defined: one for the northern section (BSRCIn), for the southern section (BSRCIs) and an average for the entire basin (BSRCI).\nBSRCI=(V \u0305_ann-V \u0305_cl)/V \u0305_cl \nwhere V \u0305 denotes the representative area average, the \u201cann\u201d denotes the annual mean for each individual year in the analysis, and \u201ccl\u201d indicates the long-term mean over the whole period 1993-2020. In general, BSRCI is defined as the relative annual anomaly from the long-term mean speed. An index close to zero means close to the average conditions a positive index indicates that the Rim current is more intense than average, or negative - if it is less intense than average. In other words, positive BSRCI would mean higher circumpolar speed, enhanced baroclinicity, enhanced dispersion of pollutants, less degree of exchange between open sea and coastal areas, intensification of the heat redistribution, etc.\nThe BSRCI is introduced in the fifth issue of the Ocean State Report (von Schuckmann et al., 2021). The Black Sea Physics Reanalysis (BLKSEA_REANALYSIS_PHYS_007_004) has been used as a data base to build the index. Details on the products are delivered in the PUM and QUID of this OMI.\n\n**CONTEXT**\n\nThe Black Sea circulation is driven by the regional winds and large freshwater river inflow in the north-western part (including the main European rivers Danube, Dnepr and Dnestr). The major cyclonic gyre encompasses the sea, referred to as Rim current. It is quasi-geostrophic and the Sverdrup balance approximately applies to it. \nThe Rim current position and speed experiences significant interannual variability (Stanev and Peneva, 2002), intensifying in winter due to the dominating severe northeastern winds in the region (Stanev et al., 2000). Consequently, this impacts the vertical stratification, Cold Intermediate Water formation, the biological activity distribution and the coastal mesoscale eddies\u2019 propagation along the current and their evolution. The higher circumpolar speed leads to enhanced dispersion of pollutants, less degree of exchange between open sea and coastal areas, enhanced baroclinicity, intensification of the heat redistribution which is important for the winter freezing in the northern zones (Simonov and Altman, 1991). Fach (2015) finds that the anchovy larval dispersal in the Black Sea is strongly controlled at the basin scale by the Rim Current and locally - by mesoscale eddies. \nSeveral recent studies of the Black Sea pollution claim that the understanding of the Rim Current behavior and how the mesoscale eddies evolve would help to predict the transport of various pollution such as oil spills (Korotenko, 2018) and floating marine litter (Stanev and Ricker, 2019) including microplastic debris (Miladinova et al., 2020) raising a serious environmental concern today. \nTo summarize, the intensity of the Black Sea Rim Current could give valuable integral measure for a great deal of physical and biogeochemical processes manifestation. Thus our objective is to develop a comprehensive index reflecting the annual mean state of the Black Sea general circulation to be used by policy makers and various end users. \n\n**CMEMS KEY FINDINGS**\n\nThe Black Sea Rim Current Index is defined as the relative annual anomaly of the long-term mean speed. The BSRCI value characterizes the annual circulation state: a value close to zero would mean close to average conditions, positive value indicates enhanced circulation, and negative value \u2013 weaker circulation than usual. The time-series of the BSRCI suggest that the Black Sea Rim current speed varies within ~30% in the period 1993-2020 with a positive trend of ~0.1 m/s/decade. In the years 2005 and 2014 there is evidently higher mean velocity, and on the opposite end are the years \u20132004, 2013 and 2016. The time series of the BSRCI gives possibility to check the relationship with the wind vorticity and validate the Sverdrup balance hypothesis. \n\n**Figure caption**\n\nTime series of the Black Sea Rim Current Index (BSRCI) at the north section (BSRCIn), south section (BSRCIs), the average (BSRCI) and its tendency for the period 1993-2020.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00326\n\n**References:**\n\n* Capet, A., A. Barth, J.-M. Beckers, and M. Gr\u00e9goire (2012), Interannual variability of Black Sea\u2019s hydrodynamics and connection to atmospheric patterns, Deep-Sea Res. Pt. II, 77 \u2013 80, 128\u2013142, doi:10.1016/j.dsr2.2012.04.010\n* Fach, B., (2015), Modeling the Influence of Hydrodynamic Processes on Anchovy Distribution and Connectivity in the Black Sea, Turkish Journal of Fisheries and Aquatic Sciences 14: 1-2, doi: 10.4194/1303-2712-v14_2_06\n* Ivanov V.A., Belokopytov V.N. (2013) Oceanography of the Black Sea. Editorial publishing board of Marine Hydrophysical Institute, 210 p, Printed by ECOSY-Gidrofizika, Sevastopol Korotaev, G., T. Oguz, A. Nikiforov, and C. Koblinsky. Seasonal, interannual, and mesoscale variability of the Black Sea upper layer circulation derived from altimeter data. Journal of Geophysical Research (Oceans). 108. C4. doi: 10. 1029/2002JC001508, 2003\n* Korotenko KA. Effects of mesoscale eddies on behavior of an oil spill resulting from an accidental deepwater blowout in the Black Sea: an assessment of the environmental impacts. PeerJ. 2018 Aug 29;6:e5448. doi: 10.7717/peerj.5448. PMID: 30186680; PMCID: PMC6119461.\n* Kubryakov, A. A., and S.V. Stanichny (2015), Seasonal and interannual variability of the Black Sea eddies and its dependence on characteristics of the large-scale circulation, Deep-Sea Res. Pt. I, 97, 80-91, https://doi.org/10.1016/j.dsr.2014.12.002\n* Miladinova S., A. Stips, D. Macias Moy, E. Garcia-Gorriz, (2020a) Pathways and mixing of the north western river waters in the Black Sea Estuarine, Coastal and Shelf Science, Volume 236, 5 May 2020, https://doi\n", "doi": "10.48670/mds-00326", "instrument": null, "keywords": "black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-circulation-boundary-blksea-rim-current-index,rim-current-intensity-index,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Rim Current Intensity Index"}, "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged": {"abstract": "**DEFINITION**\n\nThe indicator of the Kuroshio extension phase variations is based on the standardized high frequency altimeter Eddy Kinetic Energy (EKE) averaged in the area 142-149\u00b0E and 32-37\u00b0N and computed from the DUACS (https://duacs.cls.fr) delayed-time (reprocessed version DT-2021, CMEMS SEALEVEL_GLO_PHY_L4_MY_008_047, including \u201cmy\u201d (multi-year) & \u201cmyint\u201d (multi-year interim) datasets) and near real-time (CMEMS SEALEVEL_GLO_PHY_L4_NRT _008_046) altimeter sea level gridded products. The change in the reprocessed version (previously DT-2018) and the extension of the mean value of the EKE (now 27 years, previously 20 years) induce some slight changes not impacting the general variability of the Kuroshio extension (correlation coefficient of 0.988 for the total period, 0.994 for the delayed time period only). \n\n**CONTEXT**\n\nThe Kuroshio Extension is an eastward-flowing current in the subtropical western North Pacific after the Kuroshio separates from the coast of Japan at 35\u00b0N, 140\u00b0E. Being the extension of a wind-driven western boundary current, the Kuroshio Extension is characterized by a strong variability and is rich in large-amplitude meanders and energetic eddies (Niiler et al., 2003; Qiu, 2003, 2002). The Kuroshio Extension region has the largest sea surface height variability on sub-annual and decadal time scales in the extratropical North Pacific Ocean (Jayne et al., 2009; Qiu and Chen, 2010, 2005). Prediction and monitoring of the path of the Kuroshio are of huge importance for local economies as the position of the Kuroshio extension strongly determines the regions where phytoplankton and hence fish are located. Unstable (contracted) phase of the Kuroshio enhance the production of Chlorophyll (Lin et al., 2014).\n\n**CMEMS KEY FINDINGS**\n\nThe different states of the Kuroshio extension phase have been presented and validated by (Bessi\u00e8res et al., 2013) and further reported by Dr\u00e9villon et al. (2018) in the Copernicus Ocean State Report #2. Two rather different states of the Kuroshio extension are observed: an \u2018elongated state\u2019 (also called \u2018strong state\u2019) corresponding to a narrow strong steady jet, and a \u2018contracted state\u2019 (also called \u2018weak state\u2019) in which the jet is weaker and more unsteady, spreading on a wider latitudinal band. When the Kuroshio Extension jet is in a contracted (elongated) state, the upstream Kuroshio Extension path tends to become more (less) variable and regional eddy kinetic energy level tends to be higher (lower). In between these two opposite phases, the Kuroshio extension jet has many intermediate states of transition and presents either progressively weakening or strengthening trends. In 2018, the indicator reveals an elongated state followed by a weakening neutral phase since then.\n\n**Figure caption**\n\nStandardized Eddy Kinetic Energy over the Kuroshio region (following Bessi\u00e8res et al., 2013) Blue shaded areas correspond to well established strong elongated states periods, while orange shaded areas fit weak contracted states periods. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) completed by DUACS near Real Time (\u201cnrt\u201d) sea level multi-mission gridded products. The vertical red line shows the date of the transition between \u201cmyint\u201d and \u201cnrt\u201d products used.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00222\n\n**References:**\n\n* Bessi\u00e8res, L., Rio, M.H., Dufau, C., Boone, C., Pujol, M.I., 2013. Ocean state indicators from MyOcean altimeter products. Ocean Sci. 9, 545\u2013560. https://doi.org/10.5194/os-9-545-2013\n* Dr\u00e9villon, M., Legeais, J.-F., Peterson, A., Zuo, H., Rio, M.-H., Drillet, Y., Greiner, E., 2018. Western boundary currents. J. Oper. Oceanogr., Copernicus Marine Service Ocean State Report Issue 2, s60\u2013s65. https://doi.org/10.1080/1755876X.2018.1489208\n* Jayne, S.R., Hogg, N.G., Waterman, S.N., Rainville, L., Donohue, K.A., Randolph Watts, D., Tracey, K.L., McClean, J.L., Maltrud, M.E., Qiu, B., Chen, S., Hacker, P., 2009. The Kuroshio Extension and its recirculation gyres. Deep Sea Res. Part Oceanogr. Res. Pap. 56, 2088\u20132099. https://doi.org/10.1016/j.dsr.2009.08.006\n* Kelly, K.A., Small, R.J., Samelson, R.M., Qiu, B., Joyce, T.M., Kwon, Y.-O., Cronin, M.F., 2010. Western Boundary Currents and Frontal Air\u2013Sea Interaction: Gulf Stream and Kuroshio Extension. J. Clim. 23, 5644\u20135667. https://doi.org/10.1175/2010JCLI3346.1\n* Niiler, P.P., Maximenko, N.A., Panteleev, G.G., Yamagata, T., Olson, D.B., 2003. Near-surface dynamical structure of the Kuroshio Extension. J. Geophys. Res. Oceans 108. https://doi.org/10.1029/2002JC001461\n* Qiu, B., 2003. Kuroshio Extension Variability and Forcing of the Pacific Decadal Oscillations: Responses and Potential Feedback. J. Phys. Oceanogr. 33, 2465\u20132482. https://doi.org/10.1175/2459.1\n* Qiu, B., 2002. The Kuroshio Extension System: Its Large-Scale Variability and Role in the Midlatitude Ocean-Atmosphere Interaction. J. Oceanogr. 58, 57\u201375. https://doi.org/10.1023/A:1015824717293\n* Qiu, B., Chen, S., 2010. Eddy-mean flow interaction in the decadally modulating Kuroshio Extension system. Deep Sea Res. Part II Top. Stud. Oceanogr., North Pacific Oceanography after WOCE: A Commemoration to Nobuo Suginohara 57, 1098\u20131110. https://doi.org/10.1016/j.dsr2.2008.11.036\n* Qiu, B., Chen, S., 2005. Variability of the Kuroshio Extension Jet, Recirculation Gyre, and Mesoscale Eddies on Decadal Time Scales. J. Phys. Oceanogr. 35, 2090\u20132103. https://doi.org/10.1175/JPO2807.1\n", "doi": "10.48670/moi-00222", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-circulation-boundary-pacific-kuroshio-phase-area-averaged,satellite-observation,specific-turbulent-kinetic-energy-of-sea-water,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Kuroshio Phase from Observations Reprocessing"}, "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean": {"abstract": "**DEFINITION**\n\nThis ocean monitoring indicator (OMI) provides a time series of Meridional Overturning Circulation (MOC) Strength in density coordinates, area-averaged and calculated for the period from 1993 to the most recent year with the availability of reanalysis data in the Black Sea (BS). It contains 1D (time dimension) maximum MOC data computed from the Black Sea Reanalysis (BLK-REA; BLKSEA_MULTIYEAR_PHY_007_004) (Ilicak et al., 2022). The MOC is calculated by summing the meridional transport provided by the Copernicus Marine BLK-REA within density bins. The Black Sea MOC indicator represents the maximum MOC value across the basin for a density range between 22.45 and 23.85 kg/m\u00b3, which corresponds approximately to a depth interval of 25 to 80 m. To understand the overturning circulation of the Black Sea, we compute the residual meridional overturning circulation in density space. Residual overturning as a function of latitude (y) and density (\u03c3 \u0305) bins can be computed as follows:\n\u03c8^* (y,\u03c3 \u0305 )=-1/T \u222b_(t_0)^(t_1)\u2592\u222b_(x_B1)^(x_B2)\u2592\u3016\u222b_(-H)^0\u2592H[\u03c3 \u0305-\u03c3(x,y,z,t)] \u00d7\u03bd(x,y,z,t)dzdxdt,\u3017\nwhere H is the Heaviside function and \u03bd is the meridional velocity. We used 100 \u03c3_2 (potential density anomaly with reference pressure of 2000 dbar) density bins to remap the mass flux fields.\n\n**CONTEXT**\n\nThe BS meridional overturning circulation (BS-MOC) is a clockwise circulation in the northern part up to 150 m connected to cold intermediate layer (CIL) and an anticlockwise circulation in the southern part that could be connected to the influence of the Mediterranean Water inflow into the BS. In contrast to counterparts observed in the deep Atlantic and Mediterranean overturning circulations, the BS-MOC is characterized by shallowness and relatively low strength. However, its significance lies in its capacity to monitor the dynamics and evolution of the CIL which is crucial for the ventilation of the subsurface BS waters. The monitoring of the BS-MOC evolution from the BLK-REA can support the understanding how the CIL formation is affected due to climate change. The study of Black Sea MOC is relatively new. For more details, see Ilicak et al., (2022).\n\n**KEY FINDINGS**\n\nThe MOC values show a significant decline from 1994 to 2009, corresponding to the reduction in the CIL during that period. However, after 2010, the MOC in the Black Sea increased from 0.07 Sv (1 Sv = 106 m3/s) to 0.10 Sv. The CIL has nearly disappeared in recent years, as discussed by Stanev et al. (2019) and Lima et al. (2021) based on observational data and reanalysis results. The opposite pattern observed since 2010 suggests that mechanisms other than the CIL may be influencing the Black Sea MOC.\nFor the OMI we have used an updated version of the reanalysis (version E4R1) which has a different spinup compared to the OSR6 (version E3R1).\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00349\n\n**References:**\n\n* Ilicak, M., Causio, S., Ciliberti, S., Coppini, G., Lima, L., Aydogdu, A., Azevedo, D., Lecci, R., Cetin, D. U., Masina, S., Peneva, E., Gunduz, M., Pinardi, N. (2022). The Black Sea overturning circulation and its indicator of change. In: Copernicus Ocean State Report, issue 6, Journal of Operational Oceanography, 15:sup1, s64:s71; DOI: doi.org/10.1080/1755876X.2022.2095169\n* Lima, L., Ciliberti, S.A., Aydo\u011fdu, A., Masina, S., Escudier, R., Cipollone, A., Azevedo, D., Causio, S., Peneva, E., Lecci, R., Clementi, E., Jansen, E., Ilicak, M., Cret\u00ec, S., Stefanizzi, L., Palermo, F., Coppini, G. (2021). Climate Signals in the Black Sea From a Multidecadal Eddy-Resolving Reanalysis. Front. Mar. Sci. 8:710973. doi: 10.3389/fmars.2021.710973\n* Stanev, E. V., Peneva, E., Chtirkova, B. (2019). Climate change and regional ocean water mass disappearance: case of the Black Sea. J. Geophys. Res. Oceans 124, 4803\u20134819. doi: 10.1029/2019JC015076\n", "doi": "10.48670/mds-00349", "instrument": null, "keywords": "black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,ocean-meridional-overturning-streamfunction,oceanographic-geographical-features,omi-circulation-moc-blksea-area-averaged-mean,s,sla,t,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Overturning Circulation Index from Reanalysis"}, "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean": {"abstract": "**DEFINITION**\n\nTime mean meridional Eulerian streamfunctions are computed using the velocity field estimate provided by the Copernicus Marine Mediterranean Sea reanalysis over the last 35 years (1987\u20132021). The Eulerian meridional streamfunction is evaluated by integrating meridional velocity daily data first in a vertical direction, then in a meridional direction, and finally averaging over the reanalysis period.\nThe Mediterranean overturning indices are derived for the eastern and western Mediterranean Sea by computing the annual streamfunction in the two areas separated by the Strait of Sicily around 36.5\u00b0N, and then considering the associated maxima. \nIn each case a geographical constraint focused the computation on the main region of interest. For the western index, we focused on deep-water formation regions, thus excluding both the effect of shallow physical processes and the Gibraltar net inflow. For the eastern index, we investigate the Levantine and Cretan areas corresponding to the strongest meridional overturning cell locations, thus only a zonal constraint is defined.\nTime series of annual mean values is provided for the Mediterranean Sea using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021).\nMore details can be found in the Copernicus Marine Ocean State Report issue 4 (OSR4, von Schuckmann et al., 2020) Section 2.4 (Lyubartsev et al., 2020).\n\n**CONTEXT**\n\nThe western and eastern Mediterranean clockwise meridional overturning circulation is connected to deep-water formation processes. The Mediterranean Sea 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021) is used to show the interannual variability of the Meridional Overturning Index. Details on the product are delivered in the PUM and QUID of this OMI. \nThe Mediterranean Meridional Overturning Index is defined here as the maxima of the clockwise cells in the eastern and western Mediterranean Sea and is associated with deep and intermediate water mass formation processes that occur in specific areas of the basin: Gulf of Lion, Southern Adriatic Sea, Cretan Sea and Rhodes Gyre (Pinardi et al., 2015).\nAs in the global ocean, the overturning circulation of the western and eastern Mediterranean are paramount to determine the stratification of the basins (Cessi, 2019). In turn, the stratification and deep water formation mediate the exchange of oxygen and other tracers between the surface and the deep ocean (e.g., Johnson et al., 2009; Yoon et al., 2018). In this sense, the overturning indices are potential gauges of the ecosystem health of the Mediterranean Sea, and in particular they could instruct early warning indices for the Mediterranean Sea to support the Sustainable Development Goal (SDG) 13 Target 13.3.\n\n**CMEMS KEY FINDINGS**\n\nThe western and eastern Mediterranean overturning indices (WMOI and EMOI) are synthetic indices of changes in the thermohaline properties of the Mediterranean basin related to changes in the main drivers of the basin scale circulation. The western sub-basin clockwise overturning circulation is associated with the deep-water formation area of the Gulf of Lion, while the eastern clockwise meridional overturning circulation is composed of multiple cells associated with different intermediate and deep-water sources in the Levantine, Aegean, and Adriatic Seas. \nOn average, the EMOI shows higher values than the WMOI indicating a more vigorous overturning circulation in eastern Mediterranean. The difference is mostly related to the occurrence of the eastern Mediterranean transient (EMT) climatic event, and linked to a peak of the EMOI in 1992. In 1999, the difference between the two indices started to decrease because EMT water masses reached the Sicily Strait flowing into the western Mediterranean Sea (Schroeder et al., 2016). The western peak in 2006 is discussed to be linked to anomalous deep-water formation during the Western Mediterranean Transition (Smith, 2008; Schroeder et al., 2016). Thus, the WMOI and EMOI indices are a useful tool for long-term climate monitoring of overturning changes in the Mediterranean Sea. \n\n**Figure caption**\n\nTime series of Mediterranean overturning indices [Sverdrup] calculated from the annual average of the meridional streamfunction over the period 1987 to 2021. Blue: Eastern Mediterranean Overturning Index (lat<36.5\u00b0N); Red: Western Mediterranean Overturning Index (lat\u226540\u00b0N, z>300m). Product used: MEDSEA_MULTIYEAR_PHY_006_004.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00317\n\n**References:**\n\n* Cessi, P. 2019. The global overturning circulation. Ann Rev Mar Sci. 11:249\u2013270. DOI:10.1146/annurev-marine- 010318-095241. Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Aydogdu, A., Delrosso, D., Omar, M., Masina, S., Coppini, G., Pinardi, N. 2021. A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, Vol.9, pp.1060, DOI:10.3389/feart.2021.702285.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) set. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Gertman, I., Pinardi, N., Popov, Y., Hecht, A. 2006. Aegean Sea water masses during the early stages of the eastern Mediterranean climatic Transient (1988\u20131990). J Phys Oceanogr. 36(9):1841\u20131859. DOI:10.1175/JPO2940.1.\n* Johnson, K.S., Berelson, W.M., Boss, E.S., Chase, Z., Claustre, H., Emerson, S.R., Gruber, N., Ko\u0308rtzinger, A., Perry, M.J., Riser, S.C. 2009. Observing biogeochemical cycles at global scales with profiling floats and gliders: prospects for a global array. Oceanography. 22:216\u2013225. DOI:10.5670/oceanog. 2009.81.\n* Lyubartsev, V., Borile, F., Clementi, E., Masina, S., Drudi, M/. Coppini, G., Cessi, P., Pinardi, N. 2020. Interannual variability in the Eastern and Western Mediterranean Overturning Index. In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, s88\u2013s91; DOI: 10.1080/1755876X.2020.1785097.\n* Pinardi, N., Cessi, P., Borile, F., Wolfe, C.L.P. 2019. The Mediterranean Sea overturning circulation. J Phys Oceanogr. 49:1699\u20131721. DOI:10.1175/JPO-D-18-0254.1.\n* Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P., Tonani, M., Lyubartsev, V., Dobricic, S., Bonaduce, A. 2015. Mediterranean Sea large-scale, low-frequency ocean variability and water mass formation rates from 1987 to 2007: a retrospective analysis. Prog Oceanogr. 132:318\u2013332. DOI:10.1016/j.pocean.2013.11.003.\n* Roether, W., Klein, B., Hainbucher, D. 2014. Chap 6. The eastern Mediterranean transient. In: GL Eusebi Borzelli, M Gacic, P Lionello, P Malanotte-Rizzoli, editors. The Mediterranean Sea. American Geophysical Union (AGU); p. 75\u201383. DOI:10.1002/9781118847572.ch6.\n* Roether, W., Manca, B.B., Klein, B., Bregant, D., Georgopoulos, D., Beitzel, V., Kovac\u030cevic\u0301, V., Luchetta, A. 1996. Recent changes in the eastern Mediterranean deep waters. Science. 271:333\u2013335. DOI:10.1126/science.271.5247.333.\n* Schroeder, K., Chiggiato, J., Bryden, H., Borghini, M., Ismail, S.B. 2016. Abrupt climate shift in the western Mediterranean Sea. Sci Rep. 6:23009. DOI:10.1038/srep23009.\n* Smith, R.O., Bryden, H.L., Stansfield, K. 2008. Observations of new western Mediterranean deep water formation using Argo floats 2004-2006. Ocean Science, 4 (2), 133-149.\n* Von Schuckmann, K. et al. 2020. Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, DOI: 10.1080/1755876X.2020.1785097.\n* Yoon, S., Chang, K., Nam, S., Rho, T.K., Kang, D.J., Lee, T., Park, K.A., Lobanov, V., Kaplunenko, D., Tishchenko, P., Kim, K.R. 2018. Re-initiation of bottom water formation in the East Sea (Japan Sea) in a warming world. Sci Rep. 8:1576. DOI:10. 1038/s41598-018-19952-4.\n", "doi": "10.48670/mds-00317", "instrument": null, "keywords": "coastal-marine-environment,in-situ-ts-profiles,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,ocean-meridional-overturning-streamfunction,oceanographic-geographical-features,omi-circulation-moc-medsea-area-averaged-mean,sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1987-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Meridional Overturning Circulation Index from Reanalysis"}, "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged": {"abstract": "**DEFINITION**\n\nNet (positive minus negative) volume transport of Atlantic Water through the sections (see Figure 1): Faroe Shetland Channel (Water mass criteria, T > 5 \u00b0C); Barents Sea Opening (T > 3 \u00b0C) and the Fram Strait (T > 2 \u00b0C). Net volume transport of Overflow Waters (\u03c3\u03b8 >27.8 kg/m3) exiting from the Nordic Seas to the North Atlantic via the Denmark Strait and Faroe Shetland Channel. For further details, see Ch. 3.2 in von Schuckmann et al. (2018).\n\n**CONTEXT**\n\nThe poleward flow of relatively warm and saline Atlantic Water through the Nordic Seas to the Arctic Basin, balanced by the overflow waters exiting the Nordic Seas, governs the exchanges between the North Atlantic and the Arctic as well as the distribution of oceanic heat within the Arctic (e.g., Mauritzen et al., 2011; Rudels, 2012). Atlantic Water transported poleward has been found to significantly influence the sea-ice cover in the Barents Sea (Sand\u00f8 et al., 2010; \u00c5rthun et al., 2012; Onarheim et al., 2015) and near Svalbard (Piechura and Walczowski, 2009). Furthermore, Atlantic Water flow through the eastern Nordic seas and its associated heat loss and densification are important factors for the formation of overflow waters in the region (Mauritzen, 1996; Eldevik et al., 2009). These overflow waters together with those generated in the Arctic, exit the Greenland Scotland Ridge, which further contribute to the North Atlantic Deep Water (Dickson and Brown, 1994) and thus play an important role in the Atlantic Meridional Overturning Circulation (Eldevik et al., 2009; Ch. 2.3 in von Schuckmann et al., 2016). In addition to the transport of heat, the Atlantic Water also transports nutrients and zooplankton (e.g., Sundby, 2000), and it carries large amounts of ichthyoplankton of commercially important species, such as Arcto-Norwegian cod (Gadus morhua) and Norwegian spring-spawning herring (Clupea harengus) along the Norwegian coast. The Atlantic Water flow thus plays an integral part in defining both the physical and biological border between the boreal and Arctic realm. Variability of Atlantic Water flow to the Barents Sea has been found to move the position of the ice edge (Onarheim et al., 2015) as well as habitats of various species in the Barents Sea ecosystem (Fossheim et al., 2015).\n\n**CMEMS KEY FINDINGS**\n\nThe flow of Atlantic Water through the F\u00e6r\u00f8y-Shetland Channel amounts to 2.7 Sv (Berx et al., 2013). The corresponding model-based estimate was 2.5 Sv for the period 1993-2021. \nIn the Barents Sea Opening, the model indicates a long-term average net Atlantic Water inflow of 2.2 Sv, as compared with the long-term estimate from observations of 1.8 Sv (Smedsrud et al., 2013).\nIn the Fram Strait, the model data indicates a positive trend in the Atlantic Water transport to the Arctic. This trend may be explained by increased temperature in the West Spitsbergen Current during the period 2005-2010 (e.g., Walczowski et al., 2012), which caused a larger fraction of the water mass to be characterized as Atlantic Water (T > 2 \u00b0C).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00189\n\n**References:**\n\n* Berx B. Hansen B, \u00d8sterhus S, Larsen KM, Sherwin T, Jochumsen K. 2013. Combining in situ measurements and altimetry to estimate volume, heat and salt transport variability through the F\u00e6r\u00f8y-Shetland Channel. Ocean Sci. 9, 639-654\n* Dickson RR, Brown J. 1994. The production of North-Atlantic deep-water \u2013 sources, rates, and pathways. J Geophys Res Oceans. 99(C6), 12319-12341\n* Eldevik T, Nilsen JE\u00d8, Iovino D, Olsson KA, Sand\u00f8 AB, Drange H. 2009. Observed sources and variability of Nordic seas overflow. Nature Geosci. 2(6), 405-409\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan M.M, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat Climate Change. 5, 673-678.\n* Mauritzen C. 1996. Production of dense overflow waters feeding the North Atlantic across the Greenland-Scotland Ridge. 1. Evidence for a revised circulation scheme. Deep-Sea Res Part I. 43(6), 769-806\n* Mauritzen C, Hansen E, Andersson M, Berx B, Beszczynzka-M\u00f6ller A, Burud I, Christensen KH, Debernard J, de Steur L, Dodd P, et al. 2011. Closing the loop \u2013 Approaches to monitoring the state of the Arctic Mediterranean during the International Polar Year 2007-2008. Prog Oceanogr. 90, 62-89\n* Onarheim IH, Eldevik T, \u00c5rthun M, Ingvaldsen RB, Smedsrud LH. 2015. Skillful prediction of Barents Sea ice cover. Geophys Res Lett. 42(13), 5364-5371\n* Raj RP, Johannessen JA, Eldevik T, Nilsen JE\u00d8, Halo I. 2016. Quantifying mesoscale eddies in the Lofoten basin. J Geophys Res Oceans. 121. doi:10.1002/2016JC011637\n* Rudels B. 2012. Arctic Ocean circulation and variability \u2013 advection and external forcing encounter constraints and local processes. Ocean Sci. 8(2), 261-286\n* Sand\u00f8, A.B., J.E.\u00d8. Nilsen, Y. Gao and K. Lohmann, 2010: Importance of heat transport and local air-sea heat fluxes for Barents Sea climate variability. J Geophys Res. 115, C07013\n* von Schuckmann K, et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. J Oper Oceanogr. 9, 235-320\n* von Schuckmann K. 2018. Copernicus Marine Service Ocean State Report, J Oper Oceanogr. 11, sup1, S1-S142. Smedsrud LH, Esau I, Ingvaldsen RB, Eldevik T, Haugan PM, Li C, Lien VS, Olsen A, Omar AM, Otter\u00e5 OH, Risebrobakken B, Sand\u00f8 AB, Semenov VA, Sorokina SA. 2013. The role of the Barents Sea in the climate system. Rev Geophys. 51, 415-449\n* Sundby, S., 2000. Recruitment of Atlantic cod stocks in relation to temperature and advection of copepod populations. Sarsia. 85, 277-298.\n* Walczowski W, Piechura J, Goszczko I, Wieczorek P. 2012. Changes in Atlantic water properties: an important factor in the European Arctic marine climate. ICES J Mar Sys. 69(5), 864-869.\n* Piechura J, Walczowski W. 2009. Warming of the West Spitsbergen Current and sea ice north of Svalbard. Oceanol. 51(2), 147-164\n* \u00c5rthun, M., Eldevik, T., Smedsrud, L.H., Skagseth, \u00d8., Ingvaldsen, R.B., 2012. Quantifying the Influence of Atlantic Heat on Barents Sea Ice Variability and Retreat. J. Climate. 25, 4736-4743.\n", "doi": "10.48670/moi-00189", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-volume-transport-across-line,oceanographic-geographical-features,omi-circulation-voltrans-arctic-averaged,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Nordic Seas Volume Transports from Reanalysis"}, "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies": {"abstract": "**DEFINITION**\n\nThe product OMI_IBI_CURRENTS_VOLTRANS_section_integrated_anomalies is defined as the time series of annual mean volume transport calculated across a set of vertical ocean sections. These sections have been chosen to be representative of the temporal variability of various ocean currents within the IBI domain.\nThe currents that are monitored include: transport towards the North Sea through Rockall Trough (RTE) (Holliday et al., 2008; Lozier and Stewart, 2008), Canary Current (CC) (Knoll et al. 2002, Mason et al. 2011), Azores Current (AC) (Mason et al., 2011), Algerian Current (ALG) (Tintor\u00e9 et al, 1988; Benzohra and Millot, 1995; Font et al., 1998), and net transport along the 48\u00baN latitude parallel (N48) (see OMI Figure).\nTo provide ensemble-based results, four Copernicus products have been used. Among these products are three reanalysis products (GLO-REA, IBI-REA and MED-REA) and one product obtained from reprocessed observations (GLO-ARM).\n\u2022\tGLO-REA: GLOBAL_MULTIYEAR_PHY_001_030 (Reanalysis)\n\u2022\tIBI-REA: IBI_MULTIYEAR_PHY_005_002 (Reanalysis)\n\u2022\tMED-REA: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (Reprocessed observations)\n\u2022\tMED-REA: MEDSEA_MULTIYEAR_PHY_006_004MEDSEA_MULTIYEAR_PHY_006_004 (Reanalysis)\nThe time series comprises the ensemble mean (blue line), the ensemble spread (grey shaded area), and the mean transport with the sign reversed (red dashed line) to indicate the threshold of anomaly values that would entail a reversal of the current transport. Additionally, the analysis of trends in the time series at the 95% confidence interval is included in the bottom right corner of each diagram.\nDetails on the product are given in the corresponding Product User Manual (de Pascual-Collar et al., 2024a) and QUality Information Document (de Pascual-Collar et al., 2024b) as well as the CMEMS Ocean State Report: de Pascual-Collar et al., 2024c.\n\n**CONTEXT**\n\nThe IBI area is a very complex region characterized by a remarkable variety of ocean currents. Among them, Podemos destacar las que se originan como resultado del closure of the North Atlantic Drift (Mason et al., 2011; Holliday et al., 2008; Peliz et al., 2007; Bower et al., 2002; Knoll et al., 2002; P\u00e9rez et al., 2001; Jia, 2000), las corrientes subsuperficiales que fluyen hacia el norte a lo largo del talud continental (de Pascual-Collar et al., 2019; Pascual et al., 2018; Machin et al., 2010; Fricourt et al., 2007; Knoll et al., 2002; Maz\u00e9 et al., 1997; White & Bowyer, 1997). Y las corrientes de intercambio que se producen en el Estrecho de Gibraltar y el Mar de Alboran (Sotillo et al., 2016; Font et al., 1998; Benzohra and Millot, 1995; Tintor\u00e9 et al., 1988).\nThe variability of ocean currents in the IBI domain is relevant to the global thermohaline circulation and other climatic and environmental issues. For example, as discussed by Fasullo and Trenberth (2008), subtropical gyres play a crucial role in the meridional energy balance. The poleward salt transport of Mediterranean water, driven by subsurface slope currents, has significant implications for salinity anomalies in the Rockall Trough and the Nordic Seas, as studied by Holliday (2003), Holliday et al. (2008), and Bozec et al. (2011). The Algerian current serves as the sole pathway for Atlantic Water to reach the Western Mediterranean.\n\n**CMEMS KEY FINDINGS**\n\nThe volume transport time series show periods in which the different monitored currents exhibited significantly high or low variability. In this regard, we can mention the periods 1997-1998 and 2014-2015 for the RTE current, the period 2012-2014 in the N48 section, the years 2006 and 2017 for the ALG current, the year 2021 for the AC current, and the period 2009-2012 for the CC current.\nAdditionally, periods are detected where the anomalies are large enough (in absolute value) to indicate a reversal of the net transport of the current. This is the case for the years 1999, 2003, and 2012-2014 in the N48 section (with a net transport towards the north), the year 2017 in the ALC current (with net transport towards the west), and the year 2010 in the CC current (with net transport towards the north).\nThe trend analysis of the monitored currents does not detect any significant trends over the analyzed period (1993-2022). However, the confidence interval for the trend in the RTE section is on the verge of rejecting the hypothesis of no trend.\n\n**Figure caption**\n\nAnnual anomalies of cross-section volume transport in monitoring sections RTE, N48, AC, ALC, and CC. Time series computed and averaged from different Copernicus Marine products for each window (see section Definition) providing a multi-product result. The blue line represents the ensemble mean, and shaded grey areas represent the standard deviation of the ensemble. Red dashed lines depict the velocity value at which the direction of the current reverses. This aligns with the average transport value (with sign reversed) and the point where absolute transport becomes zero. The analysis of trends (at 95% confidence interval) computed in the period 1993\u20132021 is included (bottom right box). Trend lines (gray dashed line) are only included in the figures when a significant trend is obtained.\n\n**DOI (product):**\nhttps://doi.org/10.48670/mds-00351\n\n**References:**\n\n* Benzohra, M., Millot, C.: Characteristics and circulation of the surface and intermediate water masses off Algeria. Deep Sea Research Part I: Oceanographic Research Papers, 42(10), 1803-1830, https://doi.org/10.1016/0967-0637(95)00043-6, 1995.\n* Bower, A. S., Le Cann, B., Rossby, T., Zenk, T., Gould, J., Speer, K., Richardson, P. L., Prater, M. D., Zhang, H.-M.: Directly measured mid-depth circulation in the northeastern North Atlantic Ocean: Nature, 419, 6907, 603\u2013607, https://doi.org/10.1038/nature01078, 2002.\n* Bozec, A., Lozier, M. S., Chasignet, E. P., Halliwel, G. R.: On the variability of the Mediterranean Outflow Water in the North Atlantic from 1948 to 2006, J. Geophys. Res.-Oceans, 116, C09033, https://doi.org/10.1029/2011JC007191, 2011.\n* Fasullo, J. T., Trenberth, K. E.: The annual cycle of the energy budget. Part II: Meridional structures and poleward transports. Journal of Climate, 21(10), 2313-2325, https://doi.org/10.1175/2007JCLI1936.1, 2008.\n* Font, J., Millot, C., Salas, J., Juli\u00e1, A., Chic, O.: The drift of Modified Atlantic Water from the Alboran Sea to the eastern Mediterranean, Scientia Marina, 62-3, https://doi.org/10.3989/scimar.1998.62n3211, 1998.\n* Friocourt Y, Levier B, Speich S, Blanke B, Drijfhout SS. A regional numerical ocean model of the circulation in the Bay of Biscay, J. Geophys. Res.,112:C09008, https://doi.org/10.1029/2006JC003935, 2007.\n* Font, J., Millot, C., Salas, J., Juli\u00e1, A., Chic, O.: The drift of Modified Atlantic Water from the Alboran Sea to the eastern Mediterranean, Scientia Marina, 62-3, https://doi.org/10.3989/scimar.1998.62n3211, 1998.\n* Holliday, N. P., Hughes, S. L., Bacon, S., Beszczynska-M\u00f6ller, A., Hansen, B., Lav\u00edn, A., Loeng, H., Mork, K. A., \u00d8sterhus, S., Sherwin, T., Walczowski, W.: Reversal of the 1960s to 1990s freshening trend in the northeast North Atlantic and Nordic Seas, Geophys. Res. Lett., 35, L03614, https://doi.org/10.1029/2007GL032675, 2008.\n* Holliday, N. P.: Air\u2010sea interactionand circulation changes in the north- east Atlantic, J. Geophys. Res., 108(C8), 3259, https://doi.org/10.1029/2002JC001344, 2003.\n* Jia, Y.: Formation of an Azores Current Due to Mediterranean Overflow in a Modeling Study of the North Atlantic. J. Phys. Oceanogr., 30, 9, 2342\u20132358, https://doi.org/10.1175/1520-0485(2000)030<2342:FOAACD>2.0.CO;2, 2000.\n* Knoll, M., Hern\u00e1ndez-Guerra, A., Lenz, B., L\u00f3pez Laatzen, F., Mach\u0131\u0301n, F., M\u00fcller, T. J., Siedler, G.: The Eastern Boundary Current system between the Canary Islands and the African Coast, Deep-Sea Research. 49-17, 3427-3440, https://doi.org/10.1016/S0967-0645(02)00105-4, 2002.\n* Lozier, M. S., Stewart, N. M.: On the temporally varying penetration ofMediterranean overflowwaters and eastward penetration ofLabrador Sea Water, J. Phys. Oceanogr., 38,2097\u20132103, https://doi.org/10.1175/2008JPO3908.1, 2008.\n* Mach\u00edn, F., Pelegr\u00ed, J. L., Fraile-Nuez, E., V\u00e9lez-Belch\u00ed, P., L\u00f3pez-Laatzen, F., Hern\u00e1ndez-Guerra, A., Seasonal Flow Reversals of Intermediate Waters in the Canary Current System East of the Canary Islands. J. Phys. Oceanogr, 40, 1902\u20131909, https://doi.org/10.1175/2010JPO4320.1, 2010.\n* Mason, E., Colas, F., Molemaker, J., Shchepetkin, A. F., Troupin, C., McWilliams, J. C., Sangra, P.: Seasonal variability of the Canary Current: A numerical study. Journal of Geophysical Research: Oceans, 116(C6), https://doi.org/10.1029/2010JC006665, 2011.\n* Maz\u00e9, J. P., Arhan, M., Mercier, H., Volume budget of the eastern boundary layer off the Iberian Peninsula, Deep-Sea Research. 1997, 44(9-10), 1543-1574, https://doi.org/10.1016/S0967-0637(97)00038-1, 1997. Maz\u00e9, J. P., Arhan, M., Mercier, H., Volume budget of the eastern boundary layer off the Iberian Peninsula, Deep-Sea Research. 1997, 44(9-10), 1543-1574, https://doi.org/10.1016/S0967-0637(97)00038-1, 1997.\n* Pascual, A., Levier ,B., Sotillo, M., Verbrugge, N., Aznar, R., Le Cann, B.: Characterization of Mediterranean Outflow Water in the Iberia-Gulf of Biscay-Ireland region. In: von Schuckmann et al. (2018) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, https://doi.org/10.1080/1755876X.2018.1489208, 2018.\n* de Pascual-Collar, A., Aznar, R., Levirer, B., Sotillo, M.: EU Copernicus Marine Service Product Quality Information Document for Global Reanalysis Products, OMI_CURRENTS_VOLTRANS_section_integrated_anomalies, Issue 1.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-IBI-OMI-QUID-CIRCULATION-VOLTRANS_section_integrated_anomalies.pdf, 2024a.\n* de Pascual-Collar, A., Aznar, R., Levirer, B., Sotillo, M.: EU Copernicus Marine Service Product User Manual for OMI_CURRENTS_VOLTRANS_section_integrated_anomalies. Issue 1.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-IBI-OMI-PUM-CIRCULATION-VOLTRANS_section_integrated_anomalies.pdf, 2024b.\n* de Pascual-Collar, A., Aznar, R., Levier, B., Garc\u00eda-Sotillo, M.: Monitoring Main Ocean Currents of the IBI Region, in: 8th edition of the Copernicus Ocean State Report (OSR8), accepted pending of publication, 2004c.\n* de Pascual-Collar, A., Sotillo, M. G., Levier, B., Aznar, R., Lorente, P., Amo-Baladr\u00f3n, A., \u00c1lvarez-Fanjul E.: Regional circulation patterns of Mediterranean Outflow Water near the Iberian and African continental slopes. Ocean Sci., 15, 565\u2013582. https://doi.org/10.5194/os-15-565-2019, 2019.\n* Peliz, A., Dubert, J., Marchesiello, P., Teles\u2010Machado, A.: Surface circulation in the Gulf of Cadiz: Model and mean flow structure. Journal of Geophysical Research: Oceans, 112, C11, https://doi.org/10.1029/2007JC004159, 2007.\n* Perez, F. F., Castro, C. G., \u00c1lvarez-Salgado, X. A., R\u00edos, A. F.: Coupling between the Iberian basin-scale circulation and the Portugal boundary current system: a chemical study, Deep-Sea Research. I 48,1519 -1533, https://doi.org/10.1016/S0967-0637(00)00101-1, 2001.\n* Sotillo, M. G., Amo-Baladr\u00f3n, A., Padorno, E., Garcia-Ladona, E., Orfila, A., Rodr\u00edguez-Rubio, P., Conti, D., Jim\u00e9nez Madrid, J. A., de los Santos, F. J., Alvarez Fanjul E.: How is the surface Atlantic water inflow through the Gibraltar Strait forecasted? A lagrangian validation of operational oceanographic services in the Alboran Sea and the Western Mediterranean, Deep-Sea Research. 133, 100-117, https://doi.org/10.1016/j.dsr2.2016.05.020, 2016.\n* Tintore, J., La Violette, P. E., Blade, I., Cruzado, A.: A study of an intense density front in the eastern Alboran Sea: the Almeria\u2013Oran front. Journal of Physical Oceanography, 18, 10, 1384-1397, https://doi.org/10.1175/1520-0485(1988)018%3C1384:ASOAID%3E2.0.CO;2, 1988.\n* White, M., Bowyer, P.: The shelf-edge current north-west of Ireland. Annales Geophysicae 15, 1076\u20131083. https://doi.org/10.1007/s00585-997-1076-0, 1997.\n", "doi": "10.48670/mds-00351", "instrument": null, "keywords": "coastal-marine-environment,cur-armor,cur-glo-myp,cur-ibi-myp,cur-mean,cur-med-myp,cur-nws-myp,cur-std,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-circulation-voltrans-ibi-section-integrated-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Volume Transport Anomaly in Selected Vertical Sections"}, "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe Ocean Freshwater Content (OFC) is calculated according to Boyer et al. (2007)\nOFC = \u03c1(Sref, Tref, p) / \u03c1(0, Tref, p ) \u00b7 ( Sref - S) / Sref\nwhere S(x, y, z, t) and Sref (x, y, z) are actual salinity and reference salinity, respectively, and x,y,z,t are zonal, meridional, vertical and temporal coordinates, respectively. The density, \u03c1, is calculated according to the TEOS10 (IOC et al., 2010). The key issue of OFC calculations lies in how the reference salinity is defined. The climatological range of salinity in the Baltic Sea varies from the freshwater conditions in the northern and eastern parts to the oceanic water conditions in the Kattegat. We follow the Boyer et al. (2007) formulation and calculate the climatological OFC from the three-dimensional temperature (Tref) and salinity (Sref) fields averaged over the period of 1993\u20132014.\nThe method for calculating the ocean freshwater content anomaly is based on the daily mean sea water salinity fields (S) derived from the Baltic Sea reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The total freshwater content anomaly is determined using the following formula:\nOFC(t) = \u222dV OFC(x, y, z, t) dx dy dz\nThe vertical integral is computed using the static cell vertical thicknesses (dz) sourced from the reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011 dataset cmems_mod_bal_phy_my_static, spanning from the sea surface to the 300 m depth. Spatial integration is performed over the Baltic Sea spatial domain, defined as the region between 9\u00b0 - 31\u00b0 E and 53\u00b0 - 66\u00b0 N using product grid definition in cmems_mod_bal_phy_my_static. \nWe evaluate the uncertainty from the mean standard deviation of monthly mean OFC. The shaded area in the figure corresponds to the annual standard deviation of monthly mean OFC. \nLinear trend (km3y-1) has been estimated from the annual anomalies with the uncertainty of 1.96-times standard error.\n\n**CONTEXT**\nClimate warming has resulted in the intensification of the global hydrological cycle but not necessarily on the regional scale (Pratap and Markonis, 2022). The increase of net precipitation over land and sea areas, decrease of ice cover, and increase of river runoff are the main components of the global hydrological cycle that increase freshwater content in the ocean (Boyer et al., 2007) and decrease ocean salinity.\nThe Baltic Sea is one of the marginal seas where water salinity and OFC are strongly influenced by the water exchange with the North Sea. The Major Baltic Inflows (MBIs) are the most voluminous event-type sources of saline water to the Baltic Sea (Mohrholz, 2018). The frequency and intensity of the MBIs and other large volume inflows have no long-term trends but do have a multidecadal variability of about 30 years (Mohrholz, 2018; Lehmann and Post, 2015; Lehmann et al., 2017; Radtke et al., 2020). Smaller barotropic and baroclinically driven inflows transport saline water into the halocline or below it, depending on the density of the inflow water (Reissmann et al., 2009). \n\n**KEY FINDINGS**\n\nThe Baltic Sea's ocean freshwater content is exhibiting a declining trend of -37\u00b18.8 km\u00b3/year, along with decadal fluctuations as also noted by Lehmann et al. (2022). Elevated freshwater levels were recorded prior to the Major Baltic Inflows of 1993, 2002, and 2013, which subsequently led to a swift decrease in freshwater content. The lowest ocean freshwater content was recorded in 2019. Over the past four years, the freshwater content anomaly has remained comparatively stable. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00347\n\n**References:**\n\n* Boyer, T., Levitus, S., Antonov, J., Locarnini, R., Mishonov, A., Garcia, H., Josey, S.A., 2007. Changes in freshwater content in the North Atlantic Ocean 1955\u20132006. Geophysical Research Letters, 34(16), L16603. Doi: 10.1029/2007GL030126\n* IOC, SCOR and IAPSO, 2010: The international thermodynamic equation of seawater - 2010: Calculation and use of thermodynamic properties. Intergovernmental Oceanographic Commission, Manuals and Guides No. 56, UNESCO (English), 196 pp. Available from http://www.TEOS-10.org (11.10.2021).\n* Lehmann, A., Post, P., 2015. Variability of atmospheric circulation patterns associated with large volume changes of the Baltic Sea. Advances in Science and Research, 12, 219\u2013225, doi:10.5194/asr-12-219-2015\n* Lehmann, A., H\u00f6flich, K., Post, P., Myrberg, K., 2017. Pathways of deep cyclones associated with large volume changes (LVCs) and major Baltic inflows (MBIs). Journal of Marine Systems, 167, pp.11-18. doi:10.1016/j.jmarsys.2016.10.014\n* Lehmann, A., Myrberg, K., Post, P., Chubarenko, I., Dailidiene, I., Hinrichsen, H.-H., H\u00fcssy, K., Liblik, T., Meier, H. E. M., Lips, U., Bukanova, T., 2022. Salinity dynamics of the Baltic Sea. Earth System Dynamics, 13(1), pp 373 - 392. doi:10.5194/esd-13-373-2022\n* Mohrholz, V., 2018. Major Baltic inflow statistics\u2013revised. Frontiers in Marine Science, 5, p.384. doi:10.3389/fmars.2018.00384\n* Pratap, S., Markonis, Y., 2022. The response of the hydrological cycle to temperature changes in recent and distant climatic history, Progress in Earth and Planetary Science 9(1),30. doi:10.1186/s40645-022-00489-0\n* Radtke, H., Brunnabend, S.-E., Gr\u00e4we, U., Meier, H. E. M., 2020. Investigating interdecadal salinity changes in the Baltic Sea in a 1850\u20132008 hindcast simulation, Climate of the Past, 16, 1617\u20131642, doi:10.5194/cp-16-1617-2020\n* Reissmann, J. H., Burchard, H., Feistel,R., Hagen, E., Lass, H. U., Mohrholz, V., Nausch, G., Umlauf, L., Wiecczorek, G., 2009. Vertical mixing in the Baltic Sea and consequences for eutrophication a review, Progress in Oceanography, 82, 47\u201380. doi:10.1016/j.pocean.2007.10.004\n", "doi": "10.48670/mds-00347", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,ofc-balrean,ofc-balrean-lower-rmsd,ofc-balrean-upper-rmsd,omi-climate-ofc-baltic-area-averaged-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "SMHI (Sweden)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Ocean Freshwater Content Anomaly (0-300m) from Reanalysis"}, "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 300 m depth:\nOHC=\u222b_(z_1)^(z_2)\u03c1_0 c_p (T_m-T_clim )dz [1]\nwith a reference density = 1020 kg m-3 and a specific heat capacity of cp = 3980 J kg-1 \u00b0C-1 (e.g. von Schuckmann et al., 2009; Lima et al., 2020); T_m corresponds to the monthly average temperature and T_clim is the climatological temperature of the corresponding month that varies according to each individual product.\nTime series of monthly mean values area averaged ocean heat content is provided for the Black Sea (40.86\u00b0N, 46.8\u00b0N; 27.32\u00b0E, 41.96\u00b0E) and is evaluated in areas where the topography is deeper than 300m. The Azov and Marmara Seas are not considered.\nThe quality evaluation of OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies is based on the \u201cmulti-product\u201d approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean\u2019s experience (Masina et al., 2017). Three global products and one regional (Black Sea) product have been used to build an ensemble mean, and its associated ensemble spread. Details on the products are delivered in the PUM and QUID of this OMI.\n\n**CONTEXT**\n\nKnowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future.\nSeveral studies discuss a warming in the Black Sea using either observations or model results (Akpinar et al., 2017; Stanev et al. 2019; Lima et al. 2020). Using satellite sea surface temperature observations (SST), Degtyarev (2000) detected a positive temperature trend of 0.016 \u00baC years-1 in the 50-100 m layer from 1985 to 1997. From Argo floats Stanev et al. (2019) found a warming trend in the cold intermediate layer (CIL; at approximately 25 \u2013 70 m) of about 0.05 oC year-1 in recent years. The warming signal was also present in ocean heat content analyses conducted by Lima et al. (2020). Their results from the Black Sea regional reanalysis showed an increase rate of 0.880\u00b10.181 W m-2 in the upper layers (0 \u2013 200 m), which has been reflected in the disappearance of Black Sea cold intermediate layer in recent years. The newest version of reanalysis also presents a warming of 0.814\u00b10.045 W m-2 in 0 \u2013 200 m (Lima et al. (2021). This warming has been reflected in a more incidence of marine heat waves in the Black Sea over the past few years (Mohammed et al. 2022).\n\n**CMEMS KEY FINDINGS**\n\nTime series of ocean heat content anomalies present a significant interannual variability, altering between cool and warm events. This important characteristic becomes evident over the years 2012 to 2015: a minimum of ocean heat content anomaly is registered close to \u2013 2.00 x 108 J m-2 in 2012, followed by positive values around 2.00 x 108 J m-2 in 2013 and above 2.0 x 108 J m-2 most of time in 2014 and 2015. Since 2005 the Black Sea experienced an increase in ocean heat content (0-300 m), and record OHC values are noticed in 2020. The Black Sea is warming at a rate of 0.995\u00b10.084 W m-2, which is higher than the global average warming rate.\nThe increase in ocean heat content weakens the CIL, whereas its decreasing favours the CIL restoration (Akpinar et al., 2017). The years 2012 and 2017 exhibited a more evident warming interruption that induced a replenishment of the CIL (Lima et al. 2021).\n\n**Figure caption**\n\nTime series of the ensemble mean and ensemble spread (shaded area) of the monthly Black Sea averaged ocean heat content anomalies integrated over the 0-300m depth layer (J m\u20132) during Jan 2005 \u2013 December 2020. The monthly ocean heat content anomalies are defined as the deviation from the climatological ocean heat content mean (1993\u20132014) of each corresponding month. Mean trend values are also reported at the bottom right corner. The ensemble is based on different data products, i.e. Black Sea Reanalysis, global ocean reanalysis GLORYS12V1; global observational based products CORA5.2, ARMOR3D. Details on the products are given in the corresponding PUM and QUID for this OMI.\n\n**DOI (product):** \n\u00a0https://doi.org/10.48670/moi-00306\n\n**References:**\n\n* Akpinar, A., Fach, B. A., Oguz, T., 2017: Observing the subsurface thermal signature of the Black Sea cold intermediate layer with Argo profiling floats. Deep Sea Res. I Oceanogr. Res. Papers 124, 140\u2013152. doi: 10.1016/j.dsr.2017.04.002.\n* Lima, L., Peneva, E., Ciliberti, S., Masina, S., Lemieux, B., Storto, A., Chtirkova, B., 2020: Ocean heat content in the Black Sea. In: Copernicus marine service Ocean State Report, issue 4, Journal of Operational Oceanography, 13:Sup1, s41\u2013s47, doi: 10.1080/1755876X.2020.1785097.\n* Lima L., Ciliberti S. A., Aydo\u011fdu A., Masina S., Escudier R., Cipollone A., Azevedo D., Causio S., Peneva E., Lecci R., Clementi E., Jansen E., Ilicak M., Cret\u00ec S., Stefanizzi L., Palermo F., Coppini G., 2021: Climate Signals in the Black Sea From a Multidecadal Eddy-Resolving Reanalysis, Frontier in Marine Science, 8:710973, doi: 10.3389/fmars.2021.710973.\n* Masina S., A. Storto, N. Ferry, M. Valdivieso, K. Haines, M. Balmaseda, H. Zuo, M. Drevillon, L. Parent, 2017: An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project. Climate Dynamics, 49 (3): 813-841, DOI: 10.1007/s00382-015-2728-5.\n* Stanev, E. V., Peneva, E., and Chtirkova, B. 2019: Climate change and regional ocean water mass disappearance: case of the Black Sea. J. Geophys. Res. Oceans, 124, 4803\u20134819, doi: 10.1029/2019JC015076.\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 1, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann et al., 2018: Ocean heat content. In: The Copernicus Marine Environment Monitoring Service Ocean State Report, issue 2, Journal of Operational Oceanography, 11:Sup1, s1-s142, doi: 10.1080/1755876X.2018.1489208.\n* Degtyarev, A. K., 2000: Estimation of temperature increase of the Black Sea active layer during the period 1985\u2013 1997, Meteorilogiya i Gidrologiya, 6, 72\u2013 76 (in Russian).\n", "doi": "10.48670/moi-00306", "instrument": null, "keywords": "black-sea,coastal-marine-environment,in-situ-observation,integral-wrt-depth-of-sea-water-temperature-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-ohc-blksea-area-averaged-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2005-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Ocean Heat Content Anomaly (0-300m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nOcean heat content (OHC) is defined here as the deviation from a reference period (1993-20210) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 2000 m depth:\n \n With a reference density of \u03c10 = 1030 kgm-3 and a specific heat capacity of cp = 3980 J/kg\u00b0C (e.g. von Schuckmann et al., 2009)\nAveraged time series for ocean heat content and their error bars are calculated for the Iberia-Biscay-Ireland region (26\u00b0N, 56\u00b0N; 19\u00b0W, 5\u00b0E).\nThis OMI is computed using IBI-MYP, GLO-MYP reanalysis and CORA, ARMOR data from observations which provide temperatures. Where the CMEMS product for each acronym is:\n\u2022\tIBI-MYP: IBI_MULTIYEAR_PHY_005_002 (Reanalysis)\n\u2022\tGLO-MYP: GLOBAL_REANALYSIS_PHY_001_031 (Reanalysis)\n\u2022\tCORA: INSITU_GLO_TS_OA_REP_OBSERVATIONS_013_002_b (Observations)\n\u2022\tARMOR: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (Reprocessed observations)\nThe figure comprises ensemble mean (blue line) and the ensemble spread (grey shaded). Details on the product are given in the corresponding PUM for this OMI as well as the CMEMS Ocean State Report: von Schuckmann et al., 2016; von Schuckmann et al., 2018.\n\n**CONTEXT**\n\nChange in OHC is a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and can impact marine ecosystems and human livelihoods (IPCC, 2019). Additionally, OHC is one of the six Global Climate Indicators recommended by the World Meterological Organisation (WMO, 2017). \nIn the last decades, the upper North Atlantic Ocean experienced a reversal of climatic trends for temperature and salinity. While the period 1990-2004 is characterized by decadal-scale ocean warming, the period 2005-2014 shows a substantial cooling and freshening. Such variations are discussed to be linked to ocean internal dynamics, and air-sea interactions (Fox-Kemper et al., 2021; Collins et al., 2019; Robson et al 2016). Together with changes linked to the connectivity between the North Atlantic Ocean and the Mediterranean Sea (Masina et al., 2022), these variations affect the temporal evolution of regional ocean heat content in the IBI region.\nRecent studies (de Pascual-Collar et al., 2023) highlight the key role that subsurface water masses play in the OHC trends in the IBI region. These studies conclude that the vertically integrated trend is the result of different trends (both positive and negative) contributing at different layers. Therefore, the lack of representativeness of the OHC trends in the surface-intermediate waters (from 0 to 1000 m) causes the trends in intermediate and deep waters (from 1000 m to 2000 m) to be masked when they are calculated by integrating the upper layers of the ocean (from surface down to 2000 m).\n\n**CMEMS KEY FINDINGS**\n\nThe ensemble mean OHC anomaly time series over the Iberia-Biscay-Ireland region are dominated by strong year-to-year variations, and an ocean warming trend of 0.41\u00b10.4 W/m2 is barely significant.\n\n**Figure caption**\n\nTime series of annual mean area averaged ocean heat content in the Iberia-Biscay-Ireland region (basin wide) and integrated over the 0-2000m depth layer during 1993-2022: ensemble mean (blue line) and ensemble spread (shaded area). The ensemble mean is based on different data products i.e., the IBI Reanalysis, global ocean reanalysis, and the global observational based products CORA, and ARMOR3D. Trend of ensemble mean (dashed line and bottom-right box) with 95% confidence interval computed in the period 1993-2022. Details on the products are given in the corresponding PUM and QUID for this OMI.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00316\n\n**References:**\n\n* Collins M., M. Sutherland, L. Bouwer, S.-M. Cheong, T. Fr\u00f6licher, H. Jacot Des Combes, M. Koll Roxy, I. Losada, K. McInnes, B. Ratter, E. Rivera-Arriaga, R.D. Susanto, D. Swingedouw, and L. Tibig, 2019: Extremes, Abrupt Changes and Managing Risk. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 589\u2013655. https://doi.org/10.1017/9781009157964.008.\n* Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. A\u00f0algeirsd\u00f3ttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sall\u00e9e, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. P\u00e9an, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelek\u00e7i, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211\u20131362, doi: 10.1017/9781009157896.011.\n* IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (2019). In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Intergovernmental Panel on Climate Change: Geneva, Switzerland. https://www.ipcc.ch/srocc/\n* Masina, S., Pinardi, N., Cipollone, A., Banerjee, D. S., Lyubartsev, V., von Schuckmann, K., Jackson, L., Escudier, R., Clementi, E., Aydogdu, A. and Iovino D., (2022). The Atlantic Meridional Overturning Circulation forcing the mean se level in the Mediterranean Sea through the Gibraltar transport. In: Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography,15:sup1, s119\u2013s126; DOI: 10.1080/1755876X.2022.2095169\n* Potter, R. A., and Lozier, M. S. 2004: On the warming and salinification of the Mediterranean outflow waters in the North Atlantic, Geophys. Res. Lett., 31, 1\u20134, doi:10.1029/2003GL018161.\n* Robson, J., Ortega, P., Sutton, R., 2016: A reversal of climatic trends in the North Atlantic since 2005. Nature Geosci 9, 513\u2013517. https://doi.org/10.1038/ngeo2727.\n* von Schuckmann, K., F. Gaillard and P.-Y. Le Traon, 2009: Global hydrographic variability patterns during 2003-2008, Journal of Geophysical Research, 114, C09007, doi:10.1029/2008JC005237.\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WCRP (2018). Global sea-level budget 1993\u2013present. Earth Syst. Sci. Data, 10(3), 1551\u20131590. https://doi.org/10.5194/essd-10-1551-2018\n* WMO, 2017: World Meterological Organisation Bulletin, 66(2), https://public.wmo.int/en/resources/bulletin.\n", "doi": "10.48670/mds-00316", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,integral-wrt-depth-of-sea-water-potential-temperature-expressed-as-heat-content,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-ohc-ibi-area-averaged-anomalies,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "NOLOGIN", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Ocean Heat Content Anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing"}, "OMI_CLIMATE_OSC_MEDSEA_volume_mean": {"abstract": "**DEFINITION**\n\nOcean salt content (OSC) is defined and represented here as the volume average of the integral of salinity in the Mediterranean Sea from z1 = 0 m to z2 = 300 m depth:\n\u00afS=1/V \u222bV S dV\nTime series of annual mean values area averaged ocean salt content are provided for the Mediterranean Sea (30\u00b0N, 46\u00b0N; 6\u00b0W, 36\u00b0E) and are evaluated in the upper 300m excluding the shelf areas close to the coast with a depth less than 300 m. The total estimated volume is approximately 5.7e+5 km3.\n\n**CONTEXT**\n\nThe freshwater input from the land (river runoff) and atmosphere (precipitation) and inflow from the Black Sea and the Atlantic Ocean are balanced by the evaporation in the Mediterranean Sea. Evolution of the salt content may have an impact in the ocean circulation and dynamics which possibly will have implication on the entire Earth climate system. Thus monitoring changes in the salinity content is essential considering its link \u2028to changes in: the hydrological cycle, the water masses formation, the regional halosteric sea level and salt/freshwater transport, as well as for their impact on marine biodiversity.\nThe OMI_CLIMATE_OSC_MEDSEA_volume_mean is based on the \u201cmulti-product\u201d approach introduced in the seventh issue of the Ocean State Report (contribution by Aydogdu et al., 2023). Note that the estimates in Aydogdu et al. (2023) are provided monthly while here we evaluate the results per year.\nSix global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:\n\tThe Mediterranean Sea Reanalysis at 1/24\u00b0horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)\n\tFour global reanalyses at 1/4\u00b0horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031, \nGLORYS, C-GLORS, ORAS5, FOAM, DOI: https://doi.org/10.48670/moi-00024, Desportes et al., 2022)\n\tTwo observation-based products: \nCORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, DOI: https://doi.org/10.17882/46219, Szekely et al., 2022) and \nARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, DOI: https://doi.org/10.48670/moi-00052, Grenier et al., 2021). \nDetails on the products are delivered in the PUM and QUID of this OMI. \n\n**CMEMS KEY FINDINGS**\n\nThe Mediterranean Sea salt content shows a positive trend in the upper 300 m with a continuous increase over the period 1993-2019 at rate of 5.6*10-3 \u00b13.5*10-4 psu yr-1. \nThe overall ensemble mean of different products is 38.57 psu. During the early 1990s in the entire Mediterranean Sea there is a large spread in salinity with the observational based datasets showing a higher salinity, while the reanalysis products present relatively lower salinity. The maximum spread between the period 1993\u20132019 occurs in the 1990s with a value of 0.12 psu, and it decreases to as low as 0.02 psu by the end of the 2010s.\n\n**Figure caption**\n\nTime series of annual mean volume ocean salt content in the Mediterranean Sea (basin wide), integrated over the 0-300m depth layer during 1993-2019 (or longer according to data availability) including ensemble mean and ensemble spread (shaded area). The ensemble mean and associated ensemble spread are based on different data products, i.e., Mediterranean Sea Reanalysis (MED-REA), global ocean reanalysis (GLORYS, C-GLORS, ORAS5, and FOAM) and global observational based products (CORA and ARMOR3D). Details on the products are given in the corresponding PUM and QUID for this OMI.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00325\n\n**References:**\n\n* Aydogdu, A., Miraglio, P., Escudier, R., Clementi, E., Masina, S.: The dynamical role of upper layer salinity in the Mediterranean Sea, State of the Planet, accepted, 2023.\n* Desportes, C., Garric, G., R\u00e9gnier, C., Dr\u00e9villon, M., Parent, L., Drillet, Y., Masina, S., Storto, A., Mirouze, I., Cipollone, A., Zuo, H., Balmaseda, M., Peterson, D., Wood, R., Jackson, L., Mulet, S., Grenier, E., and Gounou, A.: EU Copernicus Marine Service Quality Information Document for the Global Ocean Ensemble Physics Reanalysis, GLOBAL_REANALYSIS_PHY_001_031, Issue 1.1, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-GLO-QUID-001-031.pdf (last access: 3 May 2023), 2022.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020).\n* Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Grenier, E., Verbrugge, N., Mulet, S., and Guinehut, S.: EU Copernicus Marine Service Quality Information Document for the Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD, MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, Issue 1.1, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-MOB-QUID-015-012.pdf (last access: 3 May 2023), 2021.\n* Szekely, T.: EU Copernicus Marine Service Quality Information Document for the Global Ocean-Delayed Mode gridded CORA \u2013 In-situ Observations objective analysis in Delayed Mode, INSITU_GLO_PHY_TS_OA_MY_013_052, issue 1.2, Mercator Ocean International, https://documentation.marine.copernicus.eu/QUID/CMEMS-INS-QUID-013-052.pdf (last access: 4 April 2023), 2022.\n", "doi": "10.48670/mds-00325", "instrument": null, "keywords": "coastal-marine-environment,in-situ-ts-profiles,integral-wrt-depth-of-sea-water-salinity-expressed-as-salt-content,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-osc-medsea-volume-mean,sea-level,water-mass-formation-rate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Ocean Salt Content (0-300m)"}, "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Baltic Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nThe trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nThe Baltic Sea is a relatively small semi-enclosed basin with shallow bathymetry. Different forcings have been discussed to trigger sea level variations in the Baltic Sea at different time scales. In addition to steric effects, decadal and longer sea level variability in the basin can be induced by sea water exchange with the North Sea, and in response to atmospheric forcing and climate variability (e.g., the North Atlantic Oscillation; Gr\u00e4we et al., 2019).\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Baltic Sea rises at a rate of 4.1 \uf0b1 0.8 mm/year with an acceleration of 0.10 \uf0b1\uf0200.07 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00202\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Gr\u00e4we, U., Klingbeil, K., Kelln, J., and Dangendorf, S.: Decomposing Mean Sea Level Rise in a Semi-Enclosed Basin, the Baltic Sea, J. Clim., 32, 3089\u20133108, https://doi.org/10.1175/JCLI-D-18-0174.1, 2019.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00202", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-baltic-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Black Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). \nIn the Black Sea, major drivers of change have been attributed to anthropogenic climate change (steric expansion), and mass changes induced by various water exchanges with the Mediterranean Sea, river discharge, and precipitation/evaporation changes (e.g. Volkov and Landerer, 2015). The sea level variation in the basin also shows an important interannual variability, with an increase observed before 1999 predominantly linked to steric effects, and comparable lower values afterward (Vigo et al., 2005).\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Black Sea rises at a rate of 1.00 \u00b1 0.80 mm/year with an acceleration of -0.47 \u00b1 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00215\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Vigo, I., Garcia, D., and Chao, B. F.: Change of sea level trend in the Mediterranean and Black seas, J. Mar. Res., 63, 1085\u20131100, https://doi.org/10.1357/002224005775247607, 2005.\n* Volkov, D. L. and Landerer, F. W.: Internal and external forcing of sea level variability in the Black Sea, Clim. Dyn., 45, 2633\u20132646, https://doi.org/10.1007/s00382-015-2498-0, 2015.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00215", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-blksea-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Northeast Atlantic Ocean and adjacent seas Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nUncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn this region, sea level variations are influenced by the North Atlantic Oscillation (NAO) (e.g. Delworth and Zeng, 2016) and the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). Hermans et al., 2020 also reported the dominant influence of wind on interannual sea level variability in a large part of this area. This region encompasses the Mediterranean, IBI, North-West shelf and Baltic regions with different sea level dynamics detailed in the regional indicators.\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Northeast Atlantic Ocean and adjacent seas area rises at a rate of 3.2 \u00b1 0.80 mm/year with an acceleration of 0.10 \u00b1 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00335\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Hermans, T. H. J., Le Bars, D., Katsman, C. A., Camargo, C. M. L., Gerkema, T., Calafat, F. M., Tinker, J., and Slangen, A. B. A.: Drivers of Interannual Sea Level Variability on the Northwestern European Shelf, J. Geophys. Res. Oceans, 125, e2020JC016325, https://doi.org/10.1029/2020JC016325, 2020.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Spada, G. and Melini, D.: SELEN4 (SELEN version 4.0): a Fortran program for solving the gravitationally and topographically self-consistent sea-level equation in glacial isostatic adjustment modeling, Geosci. Model Dev., 12, 5055\u20135075, https://doi.org/10.5194/gmd-12-5055-2019, 2019.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/mds-00335", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,oceanographic-geographical-features,omi-climate-sl-europe-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European Seas Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Global Ocean weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and global GIA correction of -0.3mm/yr (common global GIA correction, see Spada, 2017). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.\nThe trend uncertainty of 0.3 mm/yr is provided at 90% confidence interval using altimeter error budget (Gu\u00e9rou et al., 2022). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. \n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report (IPCC WGI, 2021), global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm/yr for the period 2006\u20132018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c).\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, global mean sea level rises at a rate of 3.4 \u00b1 0.3 mm/year. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA correction (Spada, 2017) to consider the ongoing movement of land. The observed global trend agrees with other recent estimates (Oppenheimer et al., 2019; IPCC WGI, 2021). About 30% of this rise can be attributed to ocean thermal expansion (WCRP Global Sea Level Budget Group, 2018; von Schuckmann et al., 2018), 60% is due to land ice melt from glaciers and from the Antarctic and Greenland ice sheets. The remaining 10% is attributed to changes in land water storage, such as soil moisture, surface water and groundwater. From year to year, the global mean sea level record shows significant variations related mainly to the El Ni\u00f1o Southern Oscillation (Cazenave and Cozannet, 2014).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00237\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Oppenheimer, M., Glavovic, B. C., Hinkel, J., Van de Wal, R., Magnan, A. K., Abd-Elgaward, A., Cai, R., Cifuentes Jara, M., DeConto, R. M., Ghosh, T., Hay, J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvari, Z.: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities \u2014 Special Report on the Ocean and Cryosphere in a Changing Climate: Chapter 4, 2019.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n* Gu\u00e9rou, A., Meyssignac, B., Prandi, P., Ablain, M., Ribes, A., and Bignalet-Cazalet, F.: Current observed global mean sea level rise and acceleration estimated from satellite altimetry and the associated uncertainty, EGUsphere, 1\u201343, https://doi.org/10.5194/egusphere-2022-330, 2022.\n* Cazenave, A. and Cozannet, G. L.: Sea level rise and its coastal impacts, Earths Future, 2, 15\u201334, https://doi.org/10.1002/2013EF000188, 2014.\n", "doi": "10.48670/moi-00237", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-global-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_GLOBAL_regional_trends": {"abstract": "**DEFINITION**\n\nThe sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. The product is distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). At each grid point, the trends/accelerations are estimated on the time series corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional GIA correction (GIA map of a 27 ensemble model following Spada et Melini, 2019) and adjusted from annual and semi-annual signals. Regional uncertainties on the trends estimates can be found in Prandi et al., 2021.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the IPCC 6th assessment report (IPCC WGI, 2021), global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm/yr for the period 2006\u20132018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and regional sea level change is also influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2019, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). \n\n**KEY FINDINGS**\n\nThe altimeter sea level trends over the [1993/01/01, 2023/07/06] period exhibit large-scale variations with trends up to +10 mm/yr in regions such as the western tropical Pacific Ocean. In this area, trends are mainly of thermosteric origin (Legeais et al., 2018; Meyssignac et al., 2017) in response to increased easterly winds during the last two decades associated with the decreasing Interdecadal Pacific Oscillation (IPO)/Pacific Decadal Oscillation (e.g., McGregor et al., 2012; Merrifield et al., 2012; Palanisamy et al., 2015; Rietbroek et al., 2016).\nPrandi et al. (2021) have estimated a regional altimeter sea level error budget from which they determine a regional error variance-covariance matrix and they provide uncertainties of the regional sea level trends. Over 1993-2019, the averaged local sea level trend uncertainty is around 0.83 mm/yr with local values ranging from 0.78 to 1.22 mm/yr. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00238\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nature Clim Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. P\u00f6rtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press., 2019.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., von Schuckmann, K., Melet, A., Storto, A., and Meyssignac, B.: Sea Level, Journal of Operational Oceanography, 11, s13\u2013s16, https://doi.org/10.1080/1755876X.2018.1489208, 2018.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Journal of Operational Oceanography, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* McGregor, S., Gupta, A. S., and England, M. H.: Constraining Wind Stress Products with Sea Surface Height Observations and Implications for Pacific Ocean Sea Level Trend Attribution, 25, 8164\u20138176, https://doi.org/10.1175/JCLI-D-12-00105.1, 2012.\n* Merrifield, M. A., Thompson, P. R., and Lander, M.: Multidecadal sea level anomalies and trends in the western tropical Pacific, 39, https://doi.org/10.1029/2012GL052032, 2012.\n* Meyssignac, B., Piecuch, C. G., Merchant, C. J., Racault, M.-F., Palanisamy, H., MacIntosh, C., Sathyendranath, S., and Brewin, R.: Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993\u20132011, in: Integrative Study of the Mean Sea Level and Its Components, edited by: Cazenave, A., Champollion, N., Paul, F., and Benveniste, J., Springer International Publishing, Cham, 191\u2013219, https://doi.org/10.1007/978-3-319-56490-6_9, 2017.\n* Palanisamy, H., Cazenave, A., Delcroix, T., and Meyssignac, B.: Spatial trend patterns in the Pacific Ocean sea level during the altimetry era: the contribution of thermocline depth change and internal climate variability, Ocean Dynamics, 65, 341\u2013356, https://doi.org/10.1007/s10236-014-0805-7, 2015.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Rietbroek, R., Brunnabend, S.-E., Kusche, J., Schr\u00f6ter, J., and Dahle, C.: Revisiting the contemporary sea-level budget on global and regional scales, 113, 1504\u20131509, https://doi.org/10.1073/pnas.1519132113, 2016.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat Commun, 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00238", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-global-regional-trends,satellite-observation,tendency-of-sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Mean Sea Level trend map from Observations Reprocessing"}, "OMI_CLIMATE_SL_IBI_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on regional mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Irish-Biscay-Iberian (IBI) Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT **\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn IBI region, the RMSL trend is modulated by decadal variations. As observed over the global ocean, the main actors of the long-term RMSL trend are associated with anthropogenic global/regional warming. Decadal variability is mainly linked to the strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) for decadal (20-30y) timescales (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019).\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the IBI area rises at a rate of 4.00 \uf0b1 0.80 mm/year with an acceleration of 0.14 \uf0b1\uf0200.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00252\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., D\u00f6ll, P., C\u00e1ceres, D., M\u00fcller Schmied, H., Johannessen, J. A., Nilsen, J. E. \u00d8., Raj, R. P., Forsberg, R., Sandberg S\u00f8rensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., and Benveniste, J.: Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth Syst. Sci. Data, 14, 411\u2013447, https://doi.org/10.5194/essd-14-411-2022, 2022.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00252", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-ibi-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Atlantic Iberian Biscay Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe ocean monitoring indicator of regional mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the Mediterranean Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nBeside a clear long-term trend, the regional mean sea level variation in the Mediterranean Sea shows an important interannual variability, with a high trend observed between 1993 and 1999 (nearly 8.4 mm/y) and relatively lower values afterward (nearly 2.4 mm/y between 2000 and 2022). This variability is associated with a variation of the different forcing. Steric effect has been the most important forcing before 1999 (Fenoglio-Marc, 2002; Vigo et al., 2005). Important change of the deep-water formation site also occurred in the 90\u2019s. Their influence contributed to change the temperature and salinity property of the intermediate and deep water masses. These changes in the water masses and distribution is also associated with sea surface circulation changes, as the one observed in the Ionian Sea in 1997-1998 (e.g. Ga\u010di\u0107 et al., 2011), under the influence of the North Atlantic Oscillation (NAO) and negative Atlantic Multidecadal Oscillation (AMO) phases (Incarbona et al., 2016). These circulation changes may also impact the sea level trend in the basin (Vigo et al., 2005). In 2010-2011, high regional mean sea level has been related to enhanced water mass exchange at Gibraltar, under the influence of wind forcing during the negative phase of NAO (Landerer and Volkov, 2013).The relatively high contribution of both sterodynamic (due to steric and circulation changes) and gravitational, rotational, and deformation (due to mass and water storage changes) after 2000 compared to the [1960, 1989] period is also underlined by (Calafat et al., 2022).\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the Mediterranean Sea rises at a rate of 2.5 \u00b1 0.8 mm/year with an acceleration of 0.01 \u00b1 0.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00264\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Fenoglio-Marc, L.: Long-term sea level change in the Mediterranean Sea from multi-satellite altimetry and tide gauges, Phys. Chem. Earth Parts ABC, 27, 1419\u20131431, https://doi.org/10.1016/S1474-7065(02)00084-0, 2002.\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Fenoglio-Marc, L.: Long-term sea level change in the Mediterranean Sea from multi-satellite altimetry and tide gauges, Phys. Chem. Earth Parts ABC, 27, 1419\u20131431, https://doi.org/10.1016/S1474-7065(02)00084-0, 2002.\n* Ga\u010di\u0107, M., Civitarese, G., Eusebi Borzelli, G. L., Kova\u010devi\u0107, V., Poulain, P.-M., Theocharis, A., Menna, M., Catucci, A., and Zarokanellos, N.: On the relationship between the decadal oscillations of the northern Ionian Sea and the salinity distributions in the eastern Mediterranean, J. Geophys. Res. Oceans, 116, https://doi.org/10.1029/2011JC007280, 2011.\n* Incarbona, A., Martrat, B., Mortyn, P. G., Sprovieri, M., Ziveri, P., Gogou, A., Jord\u00e0, G., Xoplaki, E., Luterbacher, J., Langone, L., Marino, G., Rodr\u00edguez-Sanz, L., Triantaphyllou, M., Di Stefano, E., Grimalt, J. O., Tranchida, G., Sprovieri, R., and Mazzola, S.: Mediterranean circulation perturbations over the last five centuries: Relevance to past Eastern Mediterranean Transient-type events, Sci. Rep., 6, 29623, https://doi.org/10.1038/srep29623, 2016.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022a.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022b.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Landerer, F. W. and Volkov, D. L.: The anatomy of recent large sea level fluctuations in the Mediterranean Sea, Geophys. Res. Lett., 40, 553\u2013557, https://doi.org/10.1002/grl.50140, 2013.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Vigo, I., Garcia, D., and Chao, B. F.: Change of sea level trend in the Mediterranean and Black seas, J. Mar. Res., 63, 1085\u20131100, https://doi.org/10.1357/002224005775247607, 2005.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n* Calafat, F. M., Frederikse, T., and Horsburgh, K.: The Sources of Sea-Level Changes in the Mediterranean Sea Since 1960, J. Geophys. Res. Oceans, 127, e2022JC019061, https://doi.org/10.1029/2022JC019061, 2022.\n", "doi": "10.48670/moi-00264", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-medsea-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057).\nThe time series of area averaged anomalies correspond to the area average of the maps in the North-West Shelf Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018) and regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered.\n\n**CONTEXT**\n\nChange in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). \nIn this region, the time series shows decadal variations. As observed over the global ocean, the main actors of the long-term sea level trend are associated with anthropogenic global/regional warming (IPCC WGII, 2021). Decadal variability is mainly linked to the Strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) for decadal (20-30y) timescales (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019). Hermans et al., 2020 also reported the dominant influence of wind on interannual sea level variability in a large part of this area. They also underscored the influence of the inverse barometer forcing in some coastal regions.\n\n**KEY FINDINGS**\n\nOver the [1993/01/01, 2023/07/06] period, the area-averaged sea level in the NWS area rises at a rate of 3.2 \uf0b1 0.8 mm/year with an acceleration of 0.09 \uf0b1\uf0200.06 mm/year2. This trend estimation is based on the altimeter measurements corrected from the global Topex-A instrumental drift at the beginning of the time series (Legeais et al., 2020) and regional GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. \n\n**Figure caption**\n\nRegional mean sea level daily evolution (in cm) over the [1993/01/01, 2022/08/04] period, from the satellite altimeter observations estimated in the North-West Shelf region, derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) altimeter sea level gridded products distributed by the Copernicus Climate Change Service (C3S), and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The annual and semi-annual periodic signals are removed, the timeseries is low-pass filtered (175 days cut-off), and the curve is corrected for the GIA using the ICE5G-VM2 GIA model (Peltier, 2004).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00271\n\n**References:**\n\n* Cazenave, A., Dieng, H.-B., Meyssignac, B., von Schuckmann, K., Decharme, B., and Berthier, E.: The rate of sea-level rise, Nat. Clim. Change, 4, 358\u2013361, https://doi.org/10.1038/nclimate2159, 2014.\n* Chafik, L., Nilsen, J. E. \u00d8., Dangendorf, S., Reverdin, G., and Frederikse, T.: North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era, Sci. Rep., 9, 1041, https://doi.org/10.1038/s41598-018-37603-6, 2019.\n* Delworth, T. L. and Zeng, F.: The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation, J. Clim., 29, 941\u2013962, https://doi.org/10.1175/JCLI-D-15-0396.1, 2016.\n* Hermans, T. H. J., Le Bars, D., Katsman, C. A., Camargo, C. M. L., Gerkema, T., Calafat, F. M., Tinker, J., and Slangen, A. B. A.: Drivers of Interannual Sea Level Variability on the Northwestern European Shelf, J. Geophys. Res. Oceans, 125, e2020JC016325, https://doi.org/10.1029/2020JC016325, 2020.\n* IPCC: AR6 Synthesis Report: Climate Change 2022, 2022a.\n* IPCC: Summary for Policymakers [H.-O. P\u00f6rtner, D.C. Roberts, E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. P\u00f6rtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Craig, S. Langsdorf, S. L\u00f6schke, V. M\u00f6ller, A. Okem, B. Rama (eds.)], 2022b.\n* IPCC: Summary for Policymakers. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)], , https://doi.org/10.1017/9781009157926.001, 2022c.\n* IPCC WGI: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* IPCC WGII: Climate Change 2021: Impacts, Adaptation and Vulnerability; Summary for Policemakers. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 2021.\n* Legeais, J. F., Llowel, W., Melet, A., and Meyssignac, B.: Evidence of the TOPEX-A altimeter instrumental anomaly and acceleration of the global mean sea level, Copernic. Mar. Serv. Ocean State Rep. Issue 4, 13, s77\u2013s82, https://doi.org/10.1080/1755876X.2021.1946240, 2020.\n* Peltier, W. R.: GLOBAL GLACIAL ISOSTASY AND THE SURFACE OF THE ICE-AGE EARTH: The ICE-5G (VM2) Model and GRACE, Annu. Rev. Earth Planet. Sci., 32, 111\u2013149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004.\n* Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993\u20132019, Sci. Data, 8, 1, https://doi.org/10.1038/s41597-020-00786-7, 2021.\n* Wang, J., Church, J. A., Zhang, X., and Chen, X.: Reconciling global mean and regional sea level change in projections and observations, Nat. Commun., 12, 990, https://doi.org/10.1038/s41467-021-21265-6, 2021.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present, Earth Syst. Sci. Data, 10, 1551\u20131590, https://doi.org/10.5194/essd-10-1551-2018, 2018.\n", "doi": "10.48670/moi-00271", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sl-northwestshelf-area-averaged-anomalies,satellite-observation,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Atlantic Shelf Mean Sea Level time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_BAL_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe OMI_CLIMATE_SST_BAL_area_averaged_anomalies product includes time series of monthly mean SST anomalies over the period 1982-2023, relative to the 1991-2020 climatology, averaged for the Baltic Sea. The SST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude.\nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the OMI product. \n\n**CONTEXT**\n\nSea Surface Temperature (SST) is an Essential Climate Variable (GCOS) that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change (GCOS 2010). The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (H\u00f8yer and She 2007; H\u00f8yer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, H\u00f8yer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2 oC from 1982 to 2012 considering all months of the year and 3-5 oC when only July-September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). \n\n**CMEMS KEY FINDINGS**\n\nThe basin-average trend of SST anomalies for Baltic Sea region amounts to 0.038\u00b10.004\u00b0C/year over the period 1982-2023 which corresponds to an average warming of 1.60\u00b0C. Adding the North Sea area, the average trend amounts to 0.029\u00b10.002\u00b0C/year over the same period, which corresponds to an average warming of 1.22\u00b0C for the entire region since 1982. \n\n**Figure caption**\n\nTime series of monthly mean (turquoise line) and annual mean (blue line) of sea surface temperature anomalies for January 1982 to December 2023, relative to the 1991-2020 mean, combined for the Baltic Sea and North Sea SST (OMI_CLIMATE_SST_BAL_area_averaged_anomalies). The data are based on the multi-year Baltic Sea L4 satellite SST reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00205\n\n**References:**\n\n* BACC II Author Team 2015. Second Assessment of Climate Change for the Baltic Sea Basin. Springer Science & Business Media, 501 pp., doi:10.1007/978-3-319-16006-1.\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* H\u00f8yer JL, She J. 2007. Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Mar. Syst., 65, 176\u2013189, doi:10.1016/j.jmarsys.2005.03.008.\n* Lehmann A, Getzlaff K, Harla\u00df J. 2011. Detailed assessment of climate variability of the Baltic Sea area for the period 1958\u20132009. Climate Res., 46, 185\u2013196, doi:10.3354/cr00876.\n* Karina von Schuckmann ((Editor)), Pierre-Yves Le Traon ((Editor)), Neville Smith ((Editor)), Ananda Pascual ((Editor)), Pierre Brasseur ((Editor)), Katja Fennel ((Editor)), Samy Djavidnia ((Editor)), Signe Aaboe, Enrique Alvarez Fanjul, Emmanuelle Autret, Lars Axell, Roland Aznar, Mario Benincasa, Abderahim Bentamy, Fredrik Boberg, Romain Bourdall\u00e9-Badie, Bruno Buongiorno Nardelli, Vittorio E. Brando, Cl\u00e9ment Bricaud, Lars-Anders Breivik, Robert J.W. Brewin, Arthur Capet, Adrien Ceschin, Stefania Ciliberti, Gianpiero Cossarini, Mar-ta de Alfonso, Alvaro de Pascual Collar, Jos de Kloe, Julie Deshayes, Charles Desportes, Marie Dr\u00e9villon, Yann Drillet, Riccardo Droghei, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Claudia Fratianni, Jes\u00fas Garc\u00eda La-fuente, Marcos Garcia Sotillo, Gilles Garric, Florent Gasparin, Riccardo Gerin, Simon Good, J\u00e9rome Gourrion, Marilaure Gr\u00e9goire, Eric Greiner, St\u00e9phanie Guinehut, Elodie Gutknecht, Fabrice Hernandez, Olga Hernandez, Jacob H\u00f8yer, Laura Jackson, Simon Jandt, Simon Josey, M\u00e9lanie Juza, John Kennedy, Zoi Kokkini, Gerasimos Korres, Mariliis K\u00f5uts, Priidik Lagemaa, Thomas Lavergne, Bernard le Cann, Jean-Fran\u00e7ois Legeais, Benedicte Lemieux-Dudon, Bruno Levier, Vidar Lien, Ilja Maljutenko, Fernando Manzano, Marta Marcos, Veselka Mari-nova, Simona Masina, Elena Mauri, Michael Mayer, Angelique Melet, Fr\u00e9d\u00e9ric M\u00e9lin, Benoit Meyssignac, Maeva Monier, Malte M\u00fcller, Sandrine Mulet, Cristina Naranjo, Giulio Notarstefano, Aur\u00e9lien Paulmier, Bego\u00f1a P\u00e9rez Gomez, Irene P\u00e9rez Gonzalez, Elisaveta Peneva, Coralie Perruche, K. Andrew Peterson, Nadia Pinardi, Andrea Pisano, Silvia Pardo, Pierre-Marie Poulain, Roshin P. Raj, Urmas Raudsepp, Michaelis Ravdas, Rebecca Reid, Marie-H\u00e9l\u00e8ne Rio, Stefano Salon, Annette Samuelsen, Michela Sammartino, Simone Sammartino, Anne Britt Sand\u00f8, Rosalia Santoleri, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Ad Stoffelen, Andrea Storto, Tanguy Szerkely, Susanne Tamm, Steffen Tietsche, Jonathan Tinker, Joaqu\u00edn Tintore, Ana Trindade, Daphne van Zanten, Luc Vandenbulcke, Anton Verhoef, Nathalie Verbrugge, Lena Viktorsson, Karina von Schuckmann, Sarah L. Wakelin, Anna Zacharioudaki & Hao Zuo (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n* Karina von Schuckmann, Pierre-Yves Le Traon, Enrique Alvarez-Fanjul, Lars Axell, Magdalena Balmaseda, Lars-Anders Breivik, Robert J. W. Brewin, Clement Bricaud, Marie Drevillon, Yann Drillet, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Marcos Garc\u00eda Sotillo, Gilles Garric, Florent Gasparin, Elodie Gutknecht, St\u00e9phanie Guinehut, Fabrice Hernandez, Melanie Juza, Bengt Karlson, Gerasimos Korres, Jean-Fran\u00e7ois Legeais, Bruno Levier, Vidar S. Lien, Rosemary Morrow, Giulio Notarstefano, Laurent Parent, \u00c1lvaro Pascual, Bego\u00f1a P\u00e9rez-G\u00f3mez, Coralie Perruche, Nadia Pinardi, Andrea Pisano, Pierre-Marie Poulain, Isabelle M. Pujol, Roshin P. Raj, Urmas Raudsepp, Herv\u00e9 Roquet, Annette Samuelsen, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Jonathan Tinker, Joaqu\u00edn Tintor\u00e9, Lena Viktorsson, Michael Ablain, Elin Almroth-Rosell, Antonio Bonaduce, Emanuela Clementi, Gianpiero Cossarini, Quentin Dagneaux, Charles Desportes, Stephen Dye, Claudia Fratianni, Simon Good, Eric Greiner, Jerome Gourrion, Mathieu Hamon, Jason Holt, Pat Hyder, John Kennedy, Fernando Manzano-Mu\u00f1oz, Ang\u00e9lique Melet, Benoit Meyssignac, Sandrine Mulet, Bruno Buongiorno Nardelli, Enda O\u2019Dea, Einar Olason, Aur\u00e9lien Paulmier, Irene P\u00e9rez-Gonz\u00e1lez, Rebecca Reid, Ma-rie-Fanny Racault, Dionysios E. Raitsos, Antonio Ramos, Peter Sykes, Tanguy Szekely & Nathalie Verbrugge (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n", "doi": "10.48670/moi-00205", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-bal-area-averaged-anomalies,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Surface Temperature anomaly time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_BAL_trend": {"abstract": "**DEFINITION**\n\nThe OMI_CLIMATE_SST_BAL_trend product includes the cumulative/net trend in sea surface temperature anomalies for the Baltic Sea from 1982-2023. The cumulative trend is the rate of change (\u00b0C/year) scaled by the number of years (42 years). The SST Level 4 analysis products that provide the input to the trend calculations are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude.\nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016. See the Copernicus Marine Service Ocean State Reports for more information on the OMI product (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). The times series of monthly anomalies have been used to calculate the trend in SST using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\n**CONTEXT**\n\nSST is an essential climate variable that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (H\u00f8yer and She 2007; H\u00f8yer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, H\u00f8yer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2oC from 1982 to 2012 considering all months of the year and 3-5oC when only July- September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). \n\n**CMEMS KEY FINDINGS**\n\nSST trends were calculated for the Baltic Sea area and the whole region including the North Sea, over the period January 1982 to December 2023. The average trend for the Baltic Sea domain (east of 9\u00b0E longitude) is 0.038\u00b0C/year, which represents an average warming of 1.60\u00b0C for the 1982-2023 period considered here. When the North Sea domain is included, the trend decreases to 0.029\u00b0C/year corresponding to an average warming of 1.22\u00b0C for the 1982-2023 period. \n**Figure caption**\n\n**Figure caption**\n\nCumulative trends in sea surface temperature anomalies calculated from 1982 to 2023 for the Baltic Sea (OMI_CLIMATE_SST_BAL_trend). Trend calculations are based on the multi-year Baltic Sea L4 SST satellite product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00206\n\n**References:**\n\n* BACC II Author Team 2015. Second Assessment of Climate Change for the Baltic Sea Basin. Springer Science & Business Media, 501 pp., doi:10.1007/978-3-319-16006-1.\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n* H\u00f8yer JL, She J. 2007. Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. J. Mar. Syst., 65, 176\u2013189, doi:10.1016/j.jmarsys.2005.03.008.\n* Lehmann A, Getzlaff K, Harla\u00df J. 2011. Detailed assessment of climate variability of the Baltic Sea area for the period 1958\u20132009. Climate Res., 46, 185\u2013196, doi:10.3354/cr00876.\n* Karina von Schuckmann ((Editor)), Pierre-Yves Le Traon ((Editor)), Neville Smith ((Editor)), Ananda Pascual ((Editor)), Pierre Brasseur ((Editor)), Katja Fennel ((Editor)), Samy Djavidnia ((Editor)), Signe Aaboe, Enrique Alvarez Fanjul, Emmanuelle Autret, Lars Axell, Roland Aznar, Mario Benincasa, Abderahim Bentamy, Fredrik Boberg, Romain Bourdall\u00e9-Badie, Bruno Buongiorno Nardelli, Vittorio E. Brando, Cl\u00e9ment Bricaud, Lars-Anders Breivik, Robert J.W. Brewin, Arthur Capet, Adrien Ceschin, Stefania Ciliberti, Gianpiero Cossarini, Marta de Alfonso, Alvaro de Pascual Collar, Jos de Kloe, Julie Deshayes, Charles Desportes, Marie Dr\u00e9villon, Yann Drillet, Riccardo Droghei, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Claudia Fratianni, Jes\u00fas Garc\u00eda Lafuente, Marcos Garcia Sotillo, Gilles Garric, Florent Gasparin, Riccardo Gerin, Simon Good, J\u00e9rome Gourrion, Marilaure Gr\u00e9goire, Eric Greiner, St\u00e9phanie Guinehut, Elodie Gutknecht, Fabrice Hernandez, Olga Hernandez, Jacob H\u00f8yer, Laura Jackson, Simon Jandt, Simon Josey, M\u00e9lanie Juza, John Kennedy, Zoi Kokkini, Gerasimos Korres, Mariliis K\u00f5uts, Priidik Lagemaa, Thomas Lavergne, Bernard le Cann, Jean-Fran\u00e7ois Legeais, Benedicte Lemieux-Dudon, Bruno Levier, Vidar Lien, Ilja Maljutenko, Fernando Manzano, Marta Marcos, Veselka Marinova, Simona Masina, Elena Mauri, Michael Mayer, Angelique Melet, Fr\u00e9d\u00e9ric M\u00e9lin, Benoit Meyssignac, Maeva Monier, Malte M\u00fcller, Sandrine Mulet, Cristina Naranjo, Giulio Notarstefano, Aur\u00e9lien Paulmier, Bego\u00f1a P\u00e9rez Gomez, Irene P\u00e9rez Gonzalez, Elisaveta Peneva, Coralie Perruche, K. Andrew Peterson, Nadia Pinardi, Andrea Pisano, Silvia Pardo, Pierre-Marie Poulain, Roshin P. Raj, Urmas Raudsepp, Michaelis Ravdas, Rebecca Reid, Marie-H\u00e9l\u00e8ne Rio, Stefano Salon, Annette Samuelsen, Michela Sammartino, Simone Sammartino, Anne Britt Sand\u00f8, Rosalia Santoleri, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Ad Stoffelen, Andrea Storto, Tanguy Szerkely, Susanne Tamm, Steffen Tietsche, Jonathan Tinker, Joaqu\u00edn Tintore, Ana Trindade, Daphne van Zanten, Luc Vandenbulcke, Anton Verhoef, Nathalie Verbrugge, Lena Viktorsson, Karina von Schuckmann, Sarah L. Wakelin, Anna Zacharioudaki & Hao Zuo (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n* Karina von Schuckmann, Pierre-Yves Le Traon, Enrique Alvarez-Fanjul, Lars Axell, Magdalena Balmaseda, Lars-Anders Breivik, Robert J. W. Brewin, Clement Bricaud, Marie Drevillon, Yann Drillet, Clotilde Dubois, Owen Embury, H\u00e9l\u00e8ne Etienne, Marcos Garc\u00eda Sotillo, Gilles Garric, Florent Gasparin, Elodie Gutknecht, St\u00e9phanie Guinehut, Fabrice Hernandez, Melanie Juza, Bengt Karlson, Gerasimos Korres, Jean-Fran\u00e7ois Legeais, Bruno Levier, Vidar S. Lien, Rosemary Morrow, Giulio Notarstefano, Laurent Parent, \u00c1lvaro Pascual, Bego\u00f1a P\u00e9rez-G\u00f3mez, Coralie Perruche, Nadia Pinardi, Andrea Pisano, Pierre-Marie Poulain, Isabelle M. Pujol, Roshin P. Raj, Urmas Raudsepp, Herv\u00e9 Roquet, Annette Samuelsen, Shubha Sathyendranath, Jun She, Simona Simoncelli, Cosimo Solidoro, Jonathan Tinker, Joaqu\u00edn Tintor\u00e9, Lena Viktorsson, Michael Ablain, Elin Almroth-Rosell, Antonio Bonaduce, Emanuela Clementi, Gianpiero Cossarini, Quentin Dagneaux, Charles Desportes, Stephen Dye, Claudia Fratianni, Simon Good, Eric Greiner, Jerome Gourrion, Mathieu Hamon, Jason Holt, Pat Hyder, John Kennedy, Fernando Manzano-Mu\u00f1oz, Ang\u00e9lique Melet, Benoit Meyssignac, Sandrine Mulet, Bruno Buongiorno Nardelli, Enda O\u2019Dea, Einar Olason, Aur\u00e9lien Paulmier, Irene P\u00e9rez-Gonz\u00e1lez, Rebecca Reid, Ma-rie-Fanny Racault, Dionysios E. Raitsos, Antonio Ramos, Peter Sykes, Tanguy Szekely & Nathalie Verbrugge (2016) The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00206", "instrument": null, "keywords": "baltic-sea,change-over-time-in-sea-surface-foundation-temperature,coastal-marine-environment,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-bal-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea Surface Temperature cumulative trend map from Observations Reprocessing"}, "OMI_CLIMATE_SST_IBI_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe omi_climate_sst_ibi_area_averaged_anomalies product for 2022 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1993 and averaged over the Iberia-Biscay-Irish Seas. The IBI SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish Seas (SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-IBI_v2.1.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps over the European North West Shelf Iberia-Biscay-Irish Seas built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019), Copernicus Climate Change Service (C3S) initiatives and Eumetsat data. Anomalies are computed against the 1993-2014 reference period.\n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018).\n\n**CMEMS KEY FINDINGS**\n\nThe overall trend in the SST anomalies in this region is 0.013 \u00b10.001 \u00b0C/year over the period 1993-2022. \n\n**Figure caption**\n\nTime series of monthly mean and 12-month filtered sea surface temperature anomalies in the Iberia-Biscay-Irish Seas during the period 1993-2022. Anomalies are relative to the climatological period 1993-2014 and built from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 satellite product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-IBI-SST.pdf). The sea surface temperature trend with its 95% confidence interval (shown in the box) is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen\u2019s method (Sen 1968).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00256\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00256", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ibi-area-averaged-anomalies,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Sea Surface Temperature time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_IBI_trend": {"abstract": "**DEFINITION**\n\nThe omi_climate_sst_ibi_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish areas over the period 1982-2023, i.e. the rate of change (\u00b0C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-IBI_v3.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish areas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. \n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\n**CMEMS KEY FINDINGS**\n\nThe overall trend in the SST anomalies in this region is 0.022 \u00b10.002 \u00b0C/year over the period 1982-2023. \n\n**Figure caption**\nSea surface temperature trend over the period 1982-2023 in the Iberia-Biscay-Irish areas. The trend is the rate of change (\u00b0C/year). The trend map in sea surface temperature is derived from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf). The trend is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen\u2019s method (Sen 1968).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00257\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389\n", "doi": "10.48670/moi-00257", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,ibi-omi-tempsal-sst-trend,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ibi-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland Sea Surface Temperature trend map from Observations Reprocessing"}, "OMI_CLIMATE_SST_IST_ARCTIC_anomaly": {"abstract": "**DEFINITION**\n\nThe OMI_CLIMATE_SST_IST_ARCTIC_anomaly product includes the 2D annual mean surface temperature anomaly for the Arctic Ocean for 2023. The annual mean surface temperature anomaly is calculated from the climatological mean estimated from 1991 to 2020, defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate,). The SST/IST Level 4 analysis that provides the input to the climatology and mean anomaly calculations are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2023. The product has a spatial resolution of 0.05 degrees in latitude and longitude. \nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly average anomalies from the reference climatology from 1991 to 2020, using the daily level 4 SST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\n**CONTEXT**\n\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al, 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\n**KEY FINDINGS**\n\nThe area average anomaly of 2023 is 1.70\u00b11.08\u00b0C (\u00b1 means 1 standard deviation in this case). The majority of anomalies are positive and exceed 2\u00b0C for most areas of the Arctic Ocean, while the largest regional anomalies exceeded 6\u00b0C. Near zero and slightly negative anomalies are observed in some areas of the Barents, Norwegian and Greenland Sea and around the Bering Strait. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00353\n\n**References:**\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42\n", "doi": "10.48670/mds-00353", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,ice-surface-temperature,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ist-arctic-anomaly,satellite-observation,sea-surface-temperature,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea and Sea Ice Surface Temperature anomaly based on reprocessed observations"}, "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies": {"abstract": "**DEFINITION **\n\nThe OMI_CLIMATE_SST_IST_ARCTIC_sst_ist_area_averaged_anomalies product includes time series of monthly mean SST/IST anomalies over the period 1982-2023, relative to the 1991-2020 climatology (30 years), averaged for the Arctic Ocean. The SST/IST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2023. The product has a spatial resolution of 0.05 degrees in latitude and longitude. \nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly average anomalies from the reference climatology from 1991 to 2020, using the daily level 4 SST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. The climatological period used is defined according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate,). See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\n**CONTEXT**\n\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al, 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\n**KEY FINDINGS**\n\nThe basin-average trend of SST/IST anomalies for the Arctic Ocean region amounts to 0.104\u00b10.005 \u00b0C/year over the period 1982-2023 (42 years) which corresponds to an average warming of 4.37\u00b0C. The 2-d map of warming trends indicates these are highest for the Beaufort Sea, Chuckchi Sea, East Siberian Sea, Laptev Sea, Kara Sea and parts of Baffin Bay. The 2d map of Arctic anomalies for 2023 reveals regional peak warming exceeding 6\u00b0C.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00323\n\n**References:**\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n", "doi": "10.48670/mds-00323", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,ice-surface-temperature,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ist-arctic-area-averaged-anomalies,satellite-observation,sea-surface-temperature,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea and Sea Ice Surface Temperature anomaly time series based on reprocessed observations"}, "OMI_CLIMATE_SST_IST_ARCTIC_trend": {"abstract": "**DEFINITION**\n\nThe OMI_CLIMATE_sst_ist_ARCTIC_sst_ist_trend product includes the cumulative/net trend in combined sea and ice surface temperature anomalies for the Arctic Ocean from 1982-2023. The cumulative trend is the rate of change (\u00b0C/year) scaled by the number of years (42 years). The SST/IST Level 4 analysis that provides the input to the trend calculations are taken from the reprocessed product SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 with a recent update to include 2023. The product has a spatial resolution of 0.05 degrees in latitude and longitude.\n\nThe OMI time series runs from Jan 1, 1982 to December 31, 2023 and is constructed by calculating monthly averages from the reference climatology defined over the period 1991-2020, according to the WMO recommendation (WMO, 2017) and recent U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate), using daily level 4 SST/IST analysis fields of the SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 product. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the temperature OMI product. The times series of monthly anomalies have been used to calculate the trend in surface temperature (combined SST and IST) using Sen\u2019s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018).\n\n**CONTEXT**\n\nSST and IST are essential climate variables that act as important input for initializing numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. Especially in the Arctic, SST/IST feedbacks amplify climate change (AMAP, 2021). In the Arctic Ocean, the surface temperatures play a crucial role for the heat exchange between the ocean and atmosphere, sea ice growth and melt processes (Key et al., 1997) in addition to weather and sea ice forecasts through assimilation into ocean and atmospheric models (Rasmussen et al., 2018). \nThe Arctic Ocean is a region that requires special attention regarding the use of satellite SST and IST records and the assessment of climatic variability due to the presence of both seawater and ice, and the large seasonal and inter-annual fluctuations in the sea ice cover which lead to increased complexity in the SST mapping of the Arctic region. Combining SST and ice surface temperature (IST) is identified as the most appropriate method for determining the surface temperature of the Arctic (Minnett et al., 2020). \nPreviously, climate trends have been estimated individually for SST and IST records (Bulgin et al., 2020; Comiso and Hall, 2014). However, this is problematic in the Arctic region due to the large temporal variability in the sea ice cover including the overlying northward migration of the ice edge on decadal timescales, and thus, the resulting climate trends are not easy to interpret (Comiso, 2003). A combined surface temperature dataset of the ocean, sea ice and the marginal ice zone (MIZ) provides a consistent climate indicator, which is important for studying climate trends in the Arctic region.\n\n**KEY FINDINGS**\n\nSST/IST trends were calculated for the Arctic Ocean over the period January 1982 to December 2023. The cumulative trends are upwards of 2\u00b0C for the greatest part of the Arctic Ocean, with the largest trends occur in the Beaufort Sea, Chuckchi Sea, East Siberian Sea, Laptev Sea, Kara Sea and parts of Baffin Bay. Zero to slightly negative trends are found at the North Atlantic part of the Arctic Ocean. The combined sea and sea ice surface temperature trend is 0.104+/-0.005\u00b0C/yr, i.e. an increase by around 4.37\u00b0C between 1982 and 2023. The 2d map of Arctic anomalies reveals regional peak warming exceeding 6\u00b0C.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00324\n\n**References:**\n\n* AMAP, 2021. Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Troms\u00f8, Norway.\n* Bulgin, C.E., Merchant, C.J., Ferreira, D., 2020. Tendencies, variability and persistence of sea surface temperature anomalies. Sci Rep 10, 7986. https://doi.org/10.1038/s41598-020-64785-9\n* Comiso, J.C., 2003. Warming Trends in the Arctic from Clear Sky Satellite Observations. Journal of Climate. https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2\n* Comiso, J.C., Hall, D.K., 2014. Climate trends in the Arctic as observed from space: Climate trends in the Arctic as observed from space. WIREs Clim Change 5, 389\u2013409. https://doi.org/10.1002/wcc.277\n* Kendall MG. 1975. Multivariate analysis. London: CharlesGriffin & Co; p. 210, 4\n* Key, J.R., Collins, J.B., Fowler, C., Stone, R.S., 1997. High-latitude surface temperature estimates from thermal satellite data. Remote Sensing of Environment 61, 302\u2013309. https://doi.org/10.1016/S0034-4257(97)89497-7\n* Minnett, P.J., Kilpatrick, K.A., Podest\u00e1, G.P., Evans, R.H., Szczodrak, M.D., Izaguirre, M.A., Williams, E.J., Walsh, S., Reynolds, R.M., Bailey, S.W., Armstrong, E.M., Vazquez-Cuervo, J., 2020. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP\u2014NASA Continuity Retrievals. Remote Sensing 12, 3369. https://doi.org/10.3390/rs12203369\n* Rasmussen, T.A.S., H\u00f8yer, J.L., Ghent, D., Bulgin, C.E., Dybkjaer, G., Ribergaard, M.H., Nielsen-Englyst, P., Madsen, K.S., 2018. Impact of Assimilation of Sea-Ice Surface Temperatures on a Coupled Ocean and Sea-Ice Model. Journal of Geophysical Research: Oceans 123, 2440\u20132460. https://doi.org/10.1002/2017JC013481\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J AmStatist Assoc. 63:1379\u20131389\n* von Schuckmann et al., 2016: The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography, Volume 9, 2016 - Issue sup2: The Copernicus Marine Environment Monitoring Service Ocean, http://dx.doi.org/10.1080/1755876X.2016.1273446.\n* von Schuckmann, K., Le Traon, P.-Y., Smith, N., Pascual, A., Brasseur, P., Fennel, K., Djavidnia, S., Aaboe, S., Fanjul, E. A., Autret, E., Axell, L., Aznar, R., Benincasa, M., Bentamy, A., Boberg, F., Bourdall\u00e9-Badie, R., Nardelli, B. B., Brando, V. E., Bricaud, C., \u2026 Zuo, H. (2018). Copernicus Marine Service Ocean State Report. Journal of Operational Oceanography, 11(sup1), S1\u2013S142. https://doi.org/10.1080/1755876X.2018.1489208\n* WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203\n", "doi": "10.48670/mds-00324", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,ice-surface-temperature,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-climate-sst-ist-arctic-trend,satellite-observation,sea-surface-temperature,target-application#seaiceinformation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Sea and Sea Ice Surface Temperature 2D trend from climatology based on reprocessed observations"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nThe omi_climate_sst_northwestshelf_area_averaged_anomalies product for 2023 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the European North West Shelf Seas. The NORTHWESTSHELF SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish areas(SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST- NORTHWESTSHELF_v3.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps over the European North West Shelf Iberai-Biscay-Irish Seas built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Anomalies are computed against the 1991-2020 reference period. \n\n**CONTEXT**\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\n**CMEMS KEY FINDINGS **\n\nThe overall trend in the SST anomalies in this region is 0.024 \u00b10.002 \u00b0C/year over the period 1982-2023. \n\n**Figure caption**\n\nTime series of monthly mean and 24-month filtered sea surface temperature anomalies in the European North West Shelf Seas during the period 1982-2023. Anomalies are relative to the climatological period 1991-2020 and built from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 satellite product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-NORTHWESTSHELF-SST.pdf). The sea surface temperature trend with its 95% confidence interval (shown in the box) is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen\u2019s method (Sen 1968).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00275\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00275", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-climate-sst-northwestshelf-area-averaged-anomalies,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf Sea Surface Temperature time series and trend from Observations Reprocessing"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_trend": {"abstract": "**DEFINITION**\n\nThe omi_climate_sst_northwestshelf_trend product includes the Sea Surface Temperature (SST) trend for the European North West Shelf Seas over the period 1982-2023, i.e. the rate of change (\u00b0C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-NORTHWESTSHELF_v3.pdf), which provided the SSTs used to compute the SST trend over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05\u00b0 grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens\u2019s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. \n\n**CONTEXT **\n\nSea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). \n\n**CMEMS KEY FINDINGS **\n\nOver the period 1982-2023, the European North West Shelf Seas mean Sea Surface Temperature (SST) increased at a rate of 0.024 \u00b1 0.002 \u00b0C/Year.\n\n**Figure caption**\n\nSea surface temperature trend over the period 1982-2023 in the European North West Shelf Seas. The trend is the rate of change (\u00b0C/year). The trend map in sea surface temperature is derived from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf). The trend is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005;) and Sen\u2019s method (Sen 1968).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00276\n\n**References:**\n\n* Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453\n* GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).\n* Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173.\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18.\n* Pezzulli, S., Stephenson, D. B., Hannachi, A., 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n", "doi": "10.48670/moi-00276", "instrument": null, "keywords": "coastal-marine-environment,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-climate-sst-northwestshelf-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf Sea Surface Temperature trend map from Observations Reprocessing"}, "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection": {"abstract": "**DEFINITION**\n\nThe North Pacific Gyre Oscillation (NPGO) is a climate pattern introduced by Di Lorenzo et al. (2008) and further reported by Tranchant et al. (2019) in the CMEMS Ocean State Report #3. The NPGO is defined as the second dominant mode of variability of Sea Surface Height (SSH) anomaly and SST anomaly in the Northeast Pacific (25\u00b0\u2013 62\u00b0N, 180\u00b0\u2013 250\u00b0E). The spatial and temporal pattern of the NPGO has been deduced over the [1950-2004] period using an empirical orthogonal function (EOF) decomposition on sea level and sea surface temperature fields produced by the Regional Ocean Modeling System (ROMS) (Di Lorenzo et al., 2008; Shchepetkin and McWilliams, 2005). Afterward, the sea level spatial pattern of the NPGO is used/projected with satellite altimeter delayed-time sea level anomalies to calculate and update the NPGO index.\nThe NPGO index disseminated on CMEMS was specifically updated from 2004 onward using up-to-date altimeter products (DT2021 version; SEALEVEL_GLO_PHY_L4_MY _008_047 CMEMS product, including \u201cmy\u201d & \u201cmyint\u201d datasets, and the near-real time SEALEVEL_GLO_PHY_L4_NRT _008_046 CMEMS product). Users that previously used the index disseminated on www.o3d.org/npgo/ web page will find slight differences induced by this update. The change in the reprocessed version (previously DT-2018) and the extension of the mean value of the SSH anomaly (now 27 years, previously 20 years) induce some slight changes not impacting the general variability of the NPGO. \n\n**CONTEXT**\n\nNPGO mode emerges as the leading mode of decadal variability for surface salinity and upper ocean nutrients (Di Lorenzo et al., 2009). The North Pacific Gyre Oscillation (NPGO) term is used because its fluctuations reflect changes in the intensity of the central and eastern branches of the North Pacific gyres circulations (Chhak et al., 2009). This index measures change in the North Pacific gyres circulation and explains key physical-biological ocean variables including temperature, salinity, sea level, nutrients, chlorophyll-a. A positive North Pacific Gyre Oscillation phase is a dipole pattern with negative SSH anomaly north of 40\u00b0N and the opposite south of 40\u00b0N. (Di Lorenzo et al., 2008) suggested that the North Pacific Gyre Oscillation is the oceanic expression of the atmospheric variability of the North Pacific Oscillation (Walker and Bliss, 1932), which has an expression in both the 2nd EOFs of SSH and Sea Surface Temperature (SST) anomalies (Ceballos et al., 2009). This finding is further supported by the recent work of (Yi et al., 2018) showing consistent pattern features between the atmospheric North Pacific Oscillation and the oceanic North Pacific Gyre Oscillation in the Coupled Model Intercomparison Project Phase 5 (CMIP5) database.\n\n**CMEMS KEY FINDINGS**\n\nThe NPGO index is presently in a negative phase, associated with a positive SSH anomaly north of 40\u00b0N and negative south of 40\u00b0N. This reflects a reduced amplitude of the central and eastern branches of the North Pacific gyre, corresponding to a reduced coastal upwelling and thus a lower sea surface salinity and concentration of nutrients. \n\n**Figure caption**\n\nNorth Pacific Gyre Oscillation (NPGO) index monthly averages. The NPGO index has been projected on normalized satellite altimeter sea level anomalies. The NPGO index is derived from (Di Lorenzo et al., 2008) before 2004, the DUACS delayed-time (reprocessed version DT-2021, \u201cmy\u201d (multi-year) dataset used when available, \u201cmyint\u201d (multi-year interim) used after) completed by DUACS near Real Time (\u201cnrt\u201d) sea level multi-mission gridded products. The vertical red lines show the date of the transition between the historical Di Lorenzo\u2019s series and the DUACS product, then between the DUACS \u201cmyint\u201d and \u201cnrt\u201d products used.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00221\n\n**References:**\n\n* Ceballos, L. I., E. Di Lorenzo, C. D. Hoyos, N. Schneider and B. Taguchi, 2009: North Pacific Gyre Oscillation Synchronizes Climate Fluctuations in the Eastern and Western Boundary Systems. Journal of Climate, 22(19) 5163-5174, doi:10.1175/2009jcli2848.1\n* Chhak, K. C., E. Di Lorenzo, N. Schneider and P. F. Cummins, 2009: Forcing of Low-Frequency Ocean Variability in the Northeast Pacific. Journal of Climate, 22(5) 1255-1276, doi:10.1175/2008jcli2639.1.\n* Di Lorenzo, E., N. Schneider, K.M. Cobb, K. Chhak, P.J.S. Franks, A.J. Miller, J.C. McWilliams, S.J. Bograd, H. Arango, E. Curchister, and others. 2008. North Pacific Gyre Oscillation links ocean climate and ecosystem change. Geophysical Research Letters 35, L08607, https://doi.org/10.1029/2007GL032838.\n* Di Lorenzo, E., J. Fiechter, N. Schneider, A. Bracco, A. J. Miller, P. J. S. Franks, S. J. Bograd, A. M. Moore, A. C. Thomas, W. Crawford, A. Pena and A. J. Hermann, 2009: Nutrient and salinity decadal variations in the central and eastern North Pacific. Geophysical Research Letters, 36, doi:10.1029/2009gl038261.\n* Di Lorenzo, E., K. M. Cobb, J. C. Furtado, N. Schneider, B. T. Anderson, A. Bracco, M. A. Alexander and D. J. Vimont, 2010: Central Pacific El Nino and decadal climate change in the North Pacific Ocean. Nature Geoscience, 3(11) 762-765, doi:10.1038/ngeo984\n* Tranchant, B. I. Pujol, E. Di Lorenzo and JF Legeais (2019). The North Pacific Gyre Oscillation. In: Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, s26\u2013s30; DOI: 10.1080/ 1755876X.2019.1633075\n* Yi, D. L., Gan, B. Wu., L., A.J. Miller, 2018. The North Pacific Gyre Oscillation and Mechanisms of Its Decadal Variability in CMIP5 Models: Journal of Climate: Vol 31, No 6, 2487-2509.\n", "doi": "10.48670/moi-00221", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-climvar-pacific-npgo-sla-eof-mode-projection,satellite-observation,tendency-of-sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Pacific Gyre Oscillation from Observations Reprocessing"}, "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies": {"abstract": "**DEFINITION**\n\nTemperature deviation from the 30-year (1991-2020) daily climatological mean temperature for the Barents Sea region (68\u00b0N - 80\u00b0N, 18\u00b0E - 55\u00b0E), relative to the difference between the daily climatological average and the 90th percentile above the climatological mean. Thus, when the index is above 1 the area is in a state of a marine heatwave, and when the index is below -1 the area is in a state of a marine cold spell, following the definition by Hobday et al. (2016). For further details, see Lien et al. (2024).\"\"\n\n**CONTEXT**\nAnomalously warm oceanic events, often termed marine heatwaves, can potentially impact the ecosystem in the affected region. The marine heatwave concept and terminology was systemized by Hobday et al. (2016), and a generally adopted definition of a marine heatwave is a period of more than five days where the temperature within a region exceeds the 90th percentile of the seasonally varying climatological average temperature for that region. The Barents Sea region has warmed considerably during the most recent climatological average period (1991-2020) due to a combination of climate warming and positive phase of regional climate variability (e.g., Lind et al., 2018 ; Skagseth et al., 2020 ; Smedsrud et al., 2022), with profound consequences for marine life where boreal species are moving northward at the expense of arctic species (e.g., Fossheim et al., 2015; Oziel et al., 2020; Husson et al., 2022).\n\n**KEY FINDINGS**\n\nThere is a clear tendency of reduced frequency and intensity of marine cold spells, and a tendency towards increased frequency and intensity of marine heat waves in the Barents Sea. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00346\n\n**References:**\n\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Clim Change. doi:10.1038/nclimate2647\n* Hobday AJ, Alexander LV, Perkins SE, Smale DA, Straub SC, Oliver ECJ, Benthuysen JA, Burrows MT, Donat MG, Feng M, Holbrook NJ, Moore PJ, Scannell HA, Gupta AS, Wernberg T. 2016. A hierarchical approach to defining marine heatwaves. Progr. Oceanogr., 141, 227-238\n* Husson B, Lind S, Fossheim M, Kato-Solvang H, Skern-Mauritzen M, P\u00e9cuchet L, Ingvaldsen RB, Dolgov AV, Primicerio R. 2022. Successive extreme climatic events lead to immediate, large-scale, and diverse responses from fish in the Arctic. Global Change Biol, 28, 3728-3744\n* Lien VS, Raj RP, Chatterjee S. 2024. Surface and bottom marine heatwave characteristics in the Barents Sea: a model study. State of the Planet (in press)\n* Lind S, Ingvaldsen RB, Furevik T. 2018. Arctic warming hotspot in the northern Barents Sea linked to declining sea-ice import. Nat Clim Change, 8, 634-639\n* Oziel L, Baudena A, Ardyna M, Massicotte P, Randelhoff A, Sallee J-B, Ingvaldsen RB, Devred E, Babin M. 2020. Faster Atlantic currents drive poleward expansion of temperate phytoplankton in the Arctic Ocean. Nat Commun., 11(1), 1705, doi:10.1038/s41467-020-15485-5\n* Skagseth \u00d8, Eldevik T, \u00c5rthun M, Asbj\u00f8rnsen H, Lien VS, Smedsrud LH. 2020. Reduced efficiency of the Barents Sea cooling machine. Nat Clim Change, doi.org/10.1038/s41558-020-0772-6\n* Smedsrud LH, Muilwijk M, Brakstad A, Madonna E, Lauvset SK, Spensberger C, Born A, Eldevik T, Drange H, Jeansson E, Li C, Olsen A, Skagseth \u00d8, Slater DA, Straneo F, V\u00e5ge K, \u00c5rthun M. 2022.\n* Nordic Seas heat loss, Atlantic inflow, and Arctic sea ice cover over the last century. Rev Geophys., 60, e2020RG000725\n", "doi": "10.48670/mds-00346", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,mhw-index-bottom,mhw-index-surface,multi-year,numerical-model,oceanographic-geographical-features,omi-extreme-mhw-arctic-area-averaged-anomalies,temperature-bottom,temperature-surface,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1991-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Marine Heatwave Index in the Barents Sea from Reanalysis"}, "OMI_EXTREME_SEASTATE_GLOBAL_swh_mean_and_P95_obs": {"abstract": "**DEFINITION**\n\nSignificant wave height (SWH), expressed in metres, is the average height of the highest one-third of waves. This OMI provides time series of seasonal mean and extreme SWH values in three oceanic regions as well as their trends from 2002 to 2020, computed from the reprocessed global L4 SWH product (WAVE_GLO_PHY_SWH_L4_MY_014_007). The extreme SWH is defined as the 95th percentile of the daily maximum of SWH over the chosen period and region. The 95th percentile represents the value below which 95% of the data points fall, indicating higher wave heights than usual. The mean and the 95th percentile of SWH are calculated for two seasons of the year to take into account the seasonal variability of waves (January, February, and March, and July, August, and September) and are in m while the trends are in cm/yr.\n\n**CONTEXT**\n\nGrasping the nature of global ocean surface waves, their variability, and their long-term interannual shifts is essential for climate research and diverse oceanic and coastal applications. The sixth IPCC Assessment Report underscores the significant role waves play in extreme sea level events (Mentaschi et al., 2017), flooding (Storlazzi et al., 2018), and coastal erosion (Barnard et al., 2017). Additionally, waves impact ocean circulation and mediate interactions between air and sea (Donelan et al., 1997) as well as sea-ice interactions (Thomas et al., 2019). Studying these long-term and interannual changes demands precise time series data spanning several decades. Until now, such records have been available only from global model reanalyses or localised in situ observations. While buoy data are valuable, they offer limited local insights and are especially scarce in the southern hemisphere. In contrast, altimeters deliver global, high-quality measurements of significant wave heights (SWH) (Gommenginger et al., 2002). The growing satellite record of SWH now facilitates more extensive global and long-term analyses. By using SWH data from a multi-mission altimetric product from 2002 to 2020, we can calculate global mean SWH and extreme SWH and evaluate their trends.\n\n**KEY FINDINGS**\n\nOver the period from 2002 to 2020, positive trends in both Significant Wave Height (SWH) and extreme SWH are mostly found in the southern hemisphere. The 95th percentile of wave heights (q95), increases more rapidly than the average values, indicating that extreme waves are growing faster than the average wave height. In the North Atlantic, SWH has increased in summertime (July August September) and decreased during the wintertime: the trend for the 95th percentile SWH is decreasing by 2.1 \u00b1 3.3 cm/year, while the mean SWH shows a decreasing trend of 2.2 \u00b1 1.76 cm/year. In the south of Australia, in boreal winter, the 95th percentile SWH is increasing at a rate of 2.6 \u00b1 1.5 cm/year (a), with the mean SWH increasing by 0.7 \u00b1 0.64 cm/year (b). Finally, in the Antarctic Circumpolar Current, also in boreal winter, the 95th percentile SWH trend is 3.2 \u00b1 2.15 cm/year (a) and the mean SWH trend is 1.4 \u00b1 0.82 cm/year (b). This variation highlights that waves evolve differently across different basins and seasons, illustrating the complex and region-specific nature of wave height trends. A full discussion regarding this OMI can be found in A. Laloue et al. (2024).\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00352\n\n**References:**\n\n* Barnard, P. L., Hoover, D., Hubbard, D. M., Snyder, A., Ludka, B. C., Allan, J., Kaminsky, G. M., Ruggiero, P., Gallien, T. W., Gabel, L., McCandless, D., Weiner, H. M., Cohn, N., Anderson, D. L., and Serafin, K. A.: Extreme oceanographic forcing and coastal response due to the 2015\u20132016 El Ni\u00f1o, Nature Communications, 8, https://doi.org/10.1038/ncomms14365, 2017.\n* Donelan, M. A., Drennan, W. M., and Katsaros, K. B.: The air\u2013sea momentum flux in conditions of wind sea and swell, Journal of Physical Oceanography, 27, 2087\u20132099, https://doi.org/10.1175/1520-0485(1997)0272.0.co;2, 1997.\n* Mentaschi, L., Vousdoukas, M. I., Voukouvalas, E., Dosio, A., and Feyen, L.: Global changes of extreme coastal wave energy fluxes triggered by intensified teleconnection patterns, Geophysical Research Letters, 44, 2416\u20132426, https://doi.org/10.1002/2016gl072488, 2017\n* Thomas, S., Babanin, A. V., Walsh, K. J. E., Stoney, L., and Heil, P.: Effect of wave-induced mixing on Antarctic sea ice in a high-resolution ocean model, Ocean Dynamics, 69, 737\u2013746, https://doi.org/10.1007/s10236-019-01268-0, 2019.\n* Gommenginger, C. P., Srokosz, M. A., Challenor, P. G., and Cotton, P. D.: Development and validation of altimeter wind speed algorithms using an extended collocated Buoy/Topex dataset, IEEE Transactions on Geoscience and Remote Sensing, 40, 251\u2013260, https://doi.org/10.1109/36.992782, 2002.\n* Storlazzi, C. D., Gingerich, S. B., van Dongeren, A., Cheriton, O. M., Swarzenski, P. W., Quataert, E., Voss, C. I., Field, D. W., Annamalai, H., Piniak, G. A., and McCall, R.: Most atolls will be uninhabitable by the mid-21st century because of sea level rise exacerbating wave-driven flooding, Science Advances, 4, https://doi.org/10.1126/sciadv.aap9741, 2018.\n* Husson, R., Charles, E.: EU Copernicus Marine Service Product User Manual for the Global Ocean L 4 Significant Wave Height From Reprocessed Satellite Measurements Product, WAVE_GLO_PHY_SWH_L4_MY_014_007, Issue 2.0, Mercator Ocean International, https://documentation.marine.copernicus.eu/PUM/CMEMS-WAV-PUM-014-005-006-007.pdf, last access: 21 July 2023, 2021 Laloue, A., Ghantous, M., Faug\u00e8re, Y., Dalphinet. A., Aouf, L.: Statistical analysis of global ocean significant wave heights from satellite altimetry over the past two decades. OSR-8 (under review)\n", "doi": "10.48670/mds-00352", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-seastate-global-swh-mean-and-p95-obs,satellite-observation,sea-surface-significant-height,sea-surface-significant-height-seasonal-number-of-observations,sea-surface-significant-height-trend-uncertainty-95percentile,sea-surface-significant-height-trend-uncertainty-mean,sea-surface-wave-significant-height-95percentile-trend,sea-surface-wave-significant-height-mean-trend,sea-surface-wave-significant-height-seasonal-95percentile,sea-surface-wave-significant-height-seasonal-mean,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2002-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean, extreme and mean significant wave height trends from satellite observations - seasonal trends"}, "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_baltic_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\n**CONTEXT**\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Baltic Sea is affected by vertical land motion due to the Glacial Isostatic Adjustment (Ludwigsen et al., 2020) and consequently relative sea level trends (as measured by tide gauges) have been shown to be strongly negative, especially in the northern part of the basin. On the other hand, Baltic Sea absolute sea level trends (from altimetry-based observations) show statistically significant positive trends (Passaro et al., 2021).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\nUp to 45 stations fulfill the completeness index criteria in this region, a few less than in 2020 (51). The spatial variation of the mean 99th percentiles follow the tidal range pattern, reaching its highest values in the northern end of the Gulf of Bothnia (e.g.: 0.81 and 0.78 m above mean sea level at the Finnish stations Kemi and Oulu, respectively) and the inner part of the Gulf of Finland (e.g.: 0.83 m above mean sea level in St. Petersburg, Russia). Smaller tides and therefore 99th percentiles are found along the southeastern coast of Sweden, between Stockholm and Gotland Island (e.g.: 0.42 m above mean sea level in Visby, Gotland Island-Sweden). Annual 99th percentiles standard deviation ranges between 3-5 cm in the South (e.g.: 3 cm in Korsor, Denmark) to 10-13 cm in the Gulf of Finland (e.g.: 12 cm in Hamina). Negative anomalies of 2022 99th percentile are observed in the northern part of the basin, in the Gulf of Bothnia, in the inner part of the Gulf of Finland and in Lolland Island stations (Denmark) reaching maximum values of -12 cm in Kemi, -9 cm in St. Petersburg and -8 cm in Rodby, respectively.. Positive anomalies of 2022 99th percentile are however found in the central and southeastern parts of the basin, with maximum values reaching 7 cm in Paldisky (Estonia) and Slipshavn (Denmark). \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00203\n\n**References:**\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1. Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Passaro M, M\u00fcller F L, Oelsmann J, Rautiainen L, Dettmering D, Hart-Davis MG, Abulaitijiang A, Andersen, OB, H\u00f8yer JL, Madsen, KS, Ringgaard IM, S\u00e4rkk\u00e4 J, Scarrott R, Schwatke C, Seitz F, Tuomi L, Restano M, and Benveniste J. 2021. Absolute Baltic Sea Level Trends in the Satellite Altimetry Era: A Revisit, Front Mar Sci, 8, 647607, https://doi.org/10.3389/FMARS.2021.647607/BIBTEX.\n", "doi": "10.48670/moi-00203", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sl-baltic-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_ibi_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\n**CONTEXT**\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Iberian Biscay Ireland region shows positive sea level trend modulated by decadal-to-multidecadal variations driven by ocean dynamics and superposed to the long-term trend (Chafik et al., 2019).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe completeness index criteria is fulfilled by 57 stations in 2021, two more than those available in 2021 (55), recently added to the multi-year product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles reflect the great tide spatial variability around the UK and the north of France. Minimum values are observed in the Irish eastern coast (e.g.: 0.66 m above mean sea level in Arklow Harbour) and the Canary Islands (e.g.: 0.93 and 0.96 m above mean sea level in Gomera and Hierro, respectively). Maximum values are observed in the Bristol and English Channels (e.g.: 6.26, 5.58 and 5.17 m above mean sea level in Newport, St. Malo and St. Helier, respectively). The annual 99th percentiles standard deviation reflects the south-north increase of storminess, ranging between 1-2 cm in the Canary Islands to 12 cm in Newport (Bristol Channel). Although less pronounced and general than in 2021, negative or close to zero anomalies of 2022 99th percentile still prevail throughout the region this year reaching up to -14 cm in St.Helier (Jersey Island, Channel Islands), or -12 cm in St. Malo. Positive anomalies of 2022 99th percentile are found in the northern part of the region (Irish eastern coast and west Scotland coast) and at a couple of stations in Southern England, with values reaching 9 cm in Bangor (Northern Ireland) and 6 cm in Portsmouth (South England). \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00253\n\n**References:**\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1. Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Chafik L, Nilsen JE\u00d8, Dangendorf S et al. 2019. North Atlantic Ocean Circulation and Decadal Sea Level Change During the Altimetry Era. Sci Rep 9, 1041. https://doi.org/10.1038/s41598-018-37603-6\n", "doi": "10.48670/moi-00253", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sl-ibi-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_medsea_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe Mediterranean Sea shows statistically significant positive sea level trends over the whole basin. However, at sub-basin scale sea level trends show spatial variability arising from local circulation (Calafat et al., 2022; Meli et al., 2023).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe completeness index criteria is fulfilled in this region by 38 stations, 26 more than in 2021, significantly increasing spatial coverage with new in situ data in the central Mediterranean Sea, primarily from Italian stations. The mean 99th percentiles reflect the spatial variability of the tide, a microtidal regime, along the Spanish, French and Italian coasts, ranging from around 0.20 m above mean sea level in Sicily and the Balearic Islands (e.g.: 0.22 m in Porto Empedocle, 0.23 m in Ibiza)) to around 0.60 m above mean sea level in the Northern Adriatic Sea (e.g.: 0.63 m in Trieste, 0.61 m in Venice). . The annual 99th percentiles standard deviation ranges between 2 cm in M\u00e1laga and Motril (South of Spain) to 8 cm in Marseille. . The 2022 99th percentile anomalies present negative values mainly along the Spanish coast (as in 2021) and in the islands of Corsica and Sardinia (Western part of the region), while positive values are observed along the Eastern French Mediterranean coast and at most of the Italian stations (closer to the central part of the region), with values ranging from -4 cm in M\u00e1laga and Motril (Spain) to +5 cm in Ancona (Italy). \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00265\n\n**References:**\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Calafat, F. M., Frederikse, T., and Horsburgh, K.: The Sources of Sea-Level Changes in the Mediterranean Sea Since 1960, J Geophys Res Oceans, 127, e2022JC019061, https://doi.org/10.1029/2022JC019061, 2022.\n* Legeais J-F, Llovel W, Melet A, and Meyssignac B. 2020. Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, In: Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, s77\u2013s82, https://doi.org/10.1080/1755876X.2020.1785097.\n* Meli M, Camargo CML, Olivieri M, Slangen ABA, and Romagnoli C. 2023. Sea-level trend variability in the Mediterranean during the 1993\u20132019 period, Front Mar Sci, 10, 1150488, https://doi.org/10.3389/FMARS.2023.1150488/BIBTEX.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. 2021. Extreme sea levels at different global warming levels. Nat. Clim. Chang. 11, 746\u2013751. https://doi.org/10.1038/s41558-021-01127-1.\n* Tebaldi, C., Ranasinghe, R., Vousdoukas, M. et al. Author Correction: Extreme sea levels at different global warming levels. Nat. Clim. Chang. 13, 588 (2023). https://doi.org/10.1038/s41558-023-01665-w.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n", "doi": "10.48670/moi-00265", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-extreme-sl-medsea-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_northwestshelf_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\n**CONTEXT**\n\nSea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990\u2019s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one metre by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. \nThe North West Shelf area presents positive sea level trends with higher trend estimates in the German Bight and around Denmark, and lower trends around the southern part of Great Britain (Dettmering et al., 2021).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe completeness index criteria is fulfilled in this region by 34 stations, eight more than in 2021 (26), most of them from Norway. The mean 99th percentiles present a large spatial variability related to the tidal pattern, with largest values found in East England and at the entrance of the English channel, and lowest values along the Danish and Swedish coasts, ranging from the 3.08 m above mean sea level in Immingan (East England) to 0.57 m above mean sea level in Ringhals (Sweden) and Helgeroa (Norway). The standard deviation of annual 99th percentiles ranges between 2-3 cm in the western part of the region (e.g.: 2 cm in Harwich, 3 cm in Dunkerke) and 7-8 cm in the eastern part and the Kattegat (e.g. 8 cm in Stenungsund, Sweden).. The 99th percentile anomalies for 2022 show positive values in Southeast England, with a maximum value of +8 cm in Lowestoft, and negative values in the eastern part of the Kattegat, reaching -8 cm in Oslo. The remaining stations exhibit minor positive or negative values. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00272\n\n**References:**\n\n* Abram, N., Gattuso, J.-P., Prakash, A., Cheng, L., Chidichimo, M. P., Crate, S., Enomoto, H., Garschagen, M., Gruber, N., Harper, S., Holland, E., Kudela, R. M., Rice, J., Steffen, K., & von Schuckmann, K. (2019). Framing and Context of the Report. In H. O. P\u00f6rtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegr\u00eda, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 73\u2013129). in press. https://www.ipcc.ch/srocc/\n* Legeais J-F, W. Llowel, A. Melet and B. Meyssignac: Evidence of the TOPEX-A Altimeter Instrumental Anomaly and Acceleration of the Global Mean Sea Level, in Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 2020, accepted.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* WCRP Global Sea Level Budget Group: Global sea-level budget 1993\u2013present. 2018. Earth Syst. Sci. Data, 10, 1551-1590, https://doi.org/10.5194/essd-10-1551-2018.\n* Vousdoukas MI, Mentaschi L, Hinkel J, et al. 2020. Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11, 2119 (2020). https://doi.org/10.1038/s41467-020-15665-3.\n* Boumis, G., Moftakhari, H. R., & Moradkhani, H. 2023. Coevolution of extreme sea levels and sea-level rise under global warming. Earth's Future, 11, e2023EF003649. https://doi. org/10.1029/2023EF003649.\n* Dettmering D, M\u00fcller FL, Oelsmann J, Passaro M, Schwatke C, Restano M, Benveniste J, and Seitz F. 2021. North SEAL: A new dataset of sea level changes in the North Sea from satellite altimetry, Earth Syst Sci Data, 13, 3733\u20133753, https://doi.org/10.5194/ESSD-13-3733-2021.\n", "doi": "10.48670/moi-00272", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-extreme-sl-northwestshelf-slev-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf sea level extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Baltic Sea has showed in the last two decades a warming trend across the whole basin with more frequent and severe heat waves (IPCC, 2022). This trend is significantly higher when considering only the summer season, which would affect the high extremes (e.g. H\u00f8yer and Karagali, 2016).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles showed in the area go from 19.6\u00baC in Tallinn station to 21.4\u00baC in Rohukula station, and the standard deviation ranges between 1\u00baC and 5.4\u00baC reached in the Estonian Coast.\nResults for this year show either positive or negative low anomalies in the Coast of Sweeden (-0.7/+0.5\u00baC) within the standard deviation margin and a general positive anomaly in the rest of the region. This anomaly is noticeable in Rohukula and Virtsu tide gauges (Estonia) with +3.9\u00baC, but inside the standard deviation in both locations. In the South Baltic two stations, GreifswalderOie and Neustadt, reach an anomaly of +2\u00baC, but around the standard deviation.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00204\n\n**References:**\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* H\u00f8yer, JL, Karagali, I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541. https://doi.org/10.1175/JCLI-D-15-0663.1\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00204", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sst-baltic-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Iberia Biscay Ireland area is characterized by a great complexity in terms of processes that take place in it. The sea surface temperature varies depending on the latitude with higher values to the South. In this area, the clear warming trend observed in other European Seas is not so evident. The northwest part is influenced by the refreshing trend in the North Atlantic, and a mild warming trend has been observed in the last decade (Pisano et al. 2020).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles showed in the area present a range from 16-20\u00baC in the Southwest of the British Isles and the English Channel, 19-21\u00baC in the West of Galician Coast, 21-23\u00baC in the south of Bay of Biscay, 23.5\u00baC in the Gulf of Cadiz to 24.5\u00baC in the Canary Island. The standard deviations are between 0.5\u00baC and 1.3\u00baC in the region except in the English Channel where the standard deviation is higher, reaching 3\u00baC.\nResults for this year show either positive or negative low anomalies below the 45\u00ba parallel, with a slight positive anomaly in the Gulf of Cadiz and the Southeast of the Bay of Biscay over 1\u00baC. In the Southwest of the British Isles and the English Channel, the anomaly is clearly positive, with some stations with an anomaly over 2\u00baC, but inside the standard deviation in the area. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00255\n\n**References:**\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Nardelli BB. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sensing 12(132). DOI: 10.3390/rs12010132.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446\n", "doi": "10.48670/moi-00255", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-sst-ibi-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Iberia Biscay Ireland sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann et al., 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe Mediterranean Sea has showed a constant increase of the SST in the last three decades across the whole basin with more frequent and severe heat waves (Juza et al., 2022). Deep analyses of the variations have displayed a non-uniform rate in space, being the warming trend more evident in the eastern Mediterranean Sea with respect to the western side. This variation rate is also changing in time over the three decades with differences between the seasons (e.g. Pastor et al. 2018; Pisano et al. 2020), being higher in Spring and Summer, which would affect the extreme values.\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles showed in the area present values from 25\u00baC in Ionian Sea and 26\u00ba in the Alboran sea and Gulf of Lion to 27\u00baC in the East of Iberian Peninsula. The standard deviation ranges from 0.6\u00baC to 1.2\u00baC in the Western Mediterranean and is around 2.2\u00baC in the Ionian Sea.\nResults for this year show a slight negative anomaly in the Ionian Sea (-1\u00baC) inside the standard deviation and a clear positive anomaly in the Western Mediterranean Sea reaching +2.2\u00baC, almost two times the standard deviation in the area.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00267\n\n**References:**\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* Pastor F, Valiente JA, Palau JL. 2018. Sea Surface Temperature in the Mediterranean: Trends and Spatial Patterns (1982\u20132016). Pure Appl. Geophys, 175: 4017. https://doi.org/10.1007/s00024-017-1739-z.\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Nardelli BB. 2020. New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations. Remote Sensing 12(132). DOI: 10.3390/rs12010132.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446.\n", "doi": "10.48670/moi-00267", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-extreme-sst-medsea-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018).\n\n**CONTEXT**\nSea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann, 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.\nThe North-West Self area comprises part of the North Atlantic, where this refreshing trend has been observed, and the North Sea, where a warming trend has been taking place in the last three decades (e.g. H\u00f8yer and Karagali, 2016).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\nThe mean 99th percentiles showed in the area present a range from 14-16\u00baC in the North of the British Isles, 16-19\u00baC in the Southwest of the North Sea to 19-21\u00baC around Denmark (Helgoland Bight, Skagerrak and Kattegat Seas). The standard deviation ranges from 0.5-1\u00baC in the North of the British Isles, 0.5-2\u00baC in the Southwest of the North Sea to 1-3\u00baC in the buoys around Denmark.\nResults for this year show either positive or negative low anomalies around their corresponding standard deviation in in the North of the British Isles (-0.5/+0.6\u00baC) and a clear positive anomaly in the other two areas reaching +2\u00baC even when they are around the standard deviation margin.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00274\n\n**References:**\n\n* Alexander MA, Scott JD, Friedland KD, Mills KE, Nye JA, Pershing AJ, Thomas AC. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth, 6(1), p.9. DOI: http://doi.org/10.1525/elementa.191.\n* Dubois C, von Schuckmann K, Josey S, Ceschin A. 2018. Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s66\u2013s70. DOI: 10.1080/1755876X.2018.1489208\n* H\u00f8yer JL, Karagali I. 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541. https://doi.org/10.1175/JCLI-D-15-0663.1\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M, Autret E, Axell L, Boberg F, Ciliberti S, Dr\u00e9villon M, Droghei R, Embury O, Gourrion J, H\u00f8yer J, Juza M, Kennedy J, Lemieux-Dudon B, Peneva E, Reid R, Simoncelli S, Storto A, Tinker J, von Schuckmann K, Wakelin SL. 2018. Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, vol 11, sup1, s5\u2013s13. DOI: 10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, \u2026 Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report. Journal of Operational Oceanography, 9(sup2), s235\u2013s320. https://doi.org/10.1080/1755876X.2016.1273446.\n", "doi": "10.48670/moi-00274", "instrument": null, "keywords": "coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,omi-extreme-sst-northwestshelf-sst-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North West Shelf sea surface temperature extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs": {"abstract": "**DEFINITION**\n\nThe OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018). \n\n**CONTEXT**\n\nProjections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023).\nIn the Baltic Sea, the particular bathymetry and geography of the basin intensify the seasonal and spatial fluctuations in wave conditions. No clear statistically significant trend in the sea state has been appreciated except a rising trend in significant wave height in winter season, linked with the reduction of sea ice coverage (Soomere, 2023; Tuomi et al., 2019).\n\n**COPERNICUS MARINE SERVICE KEY FINDINGS**\n\nThe mean 99th percentiles shown in the area are from 3 to 4 meters and the standard deviation ranges from 0.2 m to 0.4 m. \nResults for this year show a slight positive or negative anomaly in all the stations, from -0.24 m to +0.36 m, inside the margin of the standard deviation.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00199\n\n**References:**\n\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F. 2016. Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B, De Alfonso M, Zacharioudaki A, P\u00e9rez Gonz\u00e1lez I, \u00c1lvarez Fanjul E, M\u00fcller M, Marcos M, Manzano F, Korres G, Ravdas M, Tamm S. 2018. Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208.\n* Stott P. 2016. How climate change affects extreme weather events. Science, 352(6293), 1517-1518.\n* Dodet G, Piolle J-F, Quilfen Y, Abdalla S, Accensi M, Ardhuin F, et al. 2020. The sea state CCI dataset v1: Towards a sea state climate data record based on satellite observations. https://dx.doi.org/10.5194/essd-2019-253\n* Hochet A, Dodet G, S\u00e9vellec F, Bouin M-N, Patra A, & Ardhuin F. 2023. Time of emergence for altimetry-based significant wave height changes in the North Atlantic. Geophysical Research Letters, 50, e2022GL102348. https://doi.org/10.1029/2022GL102348\n* Hochet A, Dodet G, Ardhuin F, Hemer M, Young I. 2021. Sea State Decadal Variability in the North Atlantic: A Review. Climate 2021, 9, 173. https://doi.org/10.3390/cli9120173 Goda Y. 2010. Random seas and design of maritime structures. World scientific. https://doi.org/10.1142/7425.\n* Mitchell JF, Lowe J, Wood RA, & Vellinga M. 2006. Extreme events due to human-induced climate change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1845), 2117-2133.\n", "doi": "10.48670/moi-00199", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,in-situ-observation,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-extreme-wave-baltic-swh-mean-and-anomaly-obs,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "Puertos del Estado (Spain)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea significant wave height extreme variability mean and anomaly (observations)"}, "OMI_EXTREME_WAVE_BLKSEA_recent_changes": {"abstract": "**DEFINITION**\n\nExtreme wave characteristics are computed by analysing single storm events and their long-term means and trends based on the product BLKSEA_MULTIYEAR_WAV_007_006. These storm events were detected using the method proposed by Weisse and G\u00fcnther (2007). The basis of the method is the definition of a severe event threshold (SET), which we define as the 99th percentile of the significant wave height (SWH). Then, the exceedance and shortfall of the SWH at every grid point was determined and counted as a storm event. The analysis of extreme wave events also comprises the following three parameters but are not part of this OMI. The time period between each exceedance and shortfall of the SET is the lifetime of an event. The difference in the maximum SWH of each event and the SET is defined as the event intensity. The geographic area of storm events and exceedance of the SET are defined as the maximum event area. The number, lifetime, and intensity of events were averaged over each year. Finally, the yearly values were used to compute the long-term means. In addition to these parameters, we estimated the difference (anomaly) of the last available year in the multiyear dataset compared against the long-term average as well as the linear trend. To show multiyear variability, each event, fulfilling the above-described definition, is considered in the statistics. This was done independent of the events\u2019 locations within the domain. To obtain long-term trends, a linear regression was applied to the yearly time series. The statistics are based on the period 1950 to -1Y. This approach has been presented in Staneva et al. (2022) for the area of the Black Sea and was later adapted to the South Atlantic in Gramcianinov et al. (2023a, 2023b).\n\n**CONTEXT**\n\nIn the last decade, the European seas have been hit by severe storms, causing serious damage to offshore infrastructure and coastal zones and drawing public attention to the importance of having reliable and comprehensive wave forecasts/hindcasts, especially during extreme events. In addition, human activities such as the offshore wind power industry, the oil industry, and coastal recreation regularly require climate and operational information on maximum wave height at a high resolution in space and time. Thus, there is a broad consensus that a high-quality wave climatology and predictions and a deep understanding of extreme waves caused by storms could substantially contribute to coastal risk management and protection measures, thereby preventing or minimising human and material damage and losses. In this respect and in the frame of climate change, which also affects regional wind patterns and therewith the wave climate, it is important for coastal regions to gain insights into wave extreme characteristics and the related trends. These insights are crucial to initiate necessary abatement strategies especially in combination with extreme wave power statistics (see OMI OMI_EXTREME_WAVE_BLKSEA_wave_power).\n\n**KEY FINDINGS**\n\nThe yearly mean number of storm events is rather low in regions where the average annual lifetime and intensity of storms are high. In contrast, the number of events is high where their lifetime and intensity are low. While the southwest Black Sea is exposed to yearly mean storm event numbers of below the long-term spatial averages (7.3 events), it is observed that the yearly mean lifetime of the events in the same region is higher than the long-term averages. The extreme wave statistics based on the 99th percentile threshold of the significant wave height (SWH) are very similar to the wind sea wave parameter, and the swell contribution is much lower. On overall, the yearly trend of the storm events is slightly negative (-0.01 events/year) with two areas showing positive trends located in the very east and west. In terms of the mean number of storm events in 2022, a pronounced area with positive values is located along the eastern coast and another in the western basin. The rest of the Black Sea area mostly experienced less events in 2022.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00348\n\n**References:**\n\n* Gramcianinov, C.B., Staneva, J., de Camargo, R., & da Silva Dias, P.L. (2023a): Changes in extreme wave events in the southwestern South Atlantic Ocean. Ocean Dynamics, doi:10.1007/s10236-023-01575-7\n* Gramcianinov, C.B., Staneva, J., Souza, C.R.G., Linhares, P., de Camargo, R., & da Silva Dias, P.L. (2023b): Recent changes in extreme wave events in the south-western South Atlantic. In: von Schuckmann, K., Moreira, L., Le Traon, P.-Y., Gr\u00e9goire, M., Marcos, M., Staneva, J., Brasseur, P., Garric, G., Lionello, P., Karstensen, J., & Neukermans, G. (eds.): 7th edition of the Copernicus Ocean State Report (OSR7). Copernicus Publications, State Planet, 1-osr7, 12, doi:10.5194/sp-1-osr7-12-2023\n* Staneva, J., Ricker, M., Akp\u0131nar, A., Behrens, A., Giesen, R., & von Schuckmann, K. (2022): Long-term interannual changes in extreme winds and waves in the Black Sea. Copernicus Ocean State Report, Issue 6, Journal of Operational Oceanography, 15:suppl, 1-220, S.2.8., 64-72, doi:10.1080/1755876X.2022.2095169\n* Weisse, R., & G\u00fcnther, H. (2007): Wave climate and long-term changes for the Southern North Sea obtained from a high-resolution hindcast 1958\u20132002. Ocean Dynamics, 57(3), 161\u2013172, doi:10.1007/s10236-006-0094-x\n", "doi": "10.48670/mds-00348", "instrument": null, "keywords": "2022-anomaly-of-yearly-mean-number-of-wave-storm-events,black-sea,coastal-marine-environment,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-extreme-wave-blksea-recent-changes,swh,weather-climate-and-seasonal-forecasting,wind-speed,yearly-mean-number-of-wave-storm-events,yearly-trend-of-mean-number-of-wave-storm-events", "license": "proprietary", "missionStartDate": "1986-01-30T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "IO-BAS (Bulgaria)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea extreme wave events"}, "OMI_EXTREME_WAVE_BLKSEA_wave_power": {"abstract": "**DEFINITION**\n\nThe Wave Power P is defined by:\nP=(\u03c1g^2)/64\u03c0 H_s^2 T_e\nWhere \u03c1 is the surface water density, g the acceleration due to gravity, Hs the significant wave height (VHM0), and Te the wave energy period (VTM10) also abbreviated with Tm-10. The extreme statistics and related recent changes are defined by (1) the 99th percentile of the Wave Power, (2) the linear trend of 99th percentile of the Wave Power, and (3) the difference (anomaly) of the 99th percentile of the last available year in the multiyear dataset BLKSEA_MULTIYEAR_WAV_007_006 compared against the long-term average. The statistics are based on the period 1950 to -1Y and are obtained from yearly averages. This approach has been presented in Staneva et al. (2022).\n\n**CONTEXT**\n\nIn the last decade, the European seas have been hit by severe storms, causing serious damage to offshore infrastructure and coastal zones and drawing public attention to the importance of having reliable and comprehensive wave forecasts/hindcasts, especially during extreme events. In addition, human activities such as the offshore wind power industry, the oil industry, and coastal recreation regularly require climate and operational information on maximum wave height at a high resolution in space and time. Thus, there is a broad consensus that a high-quality wave climatology and predictions and a deep understanding of extreme waves caused by storms could substantially contribute to coastal risk management and protection measures, thereby preventing or minimising human and material damage and losses. In this respect, the Wave Power is a crucial quantity to plan and operate wave energy converters (WEC) and for coastal and offshore structures. For both reliable estimates of long-term Wave Power extremes are important to secure a high efficiency and to guarantee a robust and secure design, respectively.\n\n**KEY FINDINGS**\n\nThe 99th percentile of wave power mean patterns are overall consistent with the respective significant wave height pattern. The maximum 99th percentile of wave power is observed in the southwestern Black Sea. Typical values of in the eastern basin are ~20 kW/m and in the western basin ~45 kW/m. The trend of the 99th percentile of the wave power is decreasing with typical values of 50 W/m/year and a maximum of 120 W/m/year, which is equivalent to a ~25% decrease over whole period with respect to the mean. The pattern of the anomaly of the 99th percentile of wave power in 2022 correlates well with that of the wind speed anomaly in 2022, revealing a negative wave-power anomaly in the western Black Sea (P_20200.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was negative (-0.21% year-1) inside the North Atlantic gyre relative to 2000-01-01 values. This is a slightly lower rate of change compared with the -0.24% trend for the 1997-2020 period but is still statistically significant (p<0.05).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00226\n\n**References:**\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00226", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-nag-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean": {"abstract": "**DEFINITION**\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data inside the gyre. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\n**CONTEXT**\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\n**CMEMS KEY FINDINGS**\n\nThe trend in the North Pacific gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 1.75% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 2.17% reported for the 1997-2020 period. The trend is statistically significant (p<0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was negative (-0.26% year-1) in the North Pacific gyre relative to 2000-01-01 values. This trend is slightly less negative than the trend of -0.31% year-1 for the 1997-2020 period, though the sign of the trend remains unchanged and is statistically significant (p<0.05). It must be noted that the difference is small and within the uncertainty of the calculations, indicating that the trend is significant, however there may be no change associated with the timeseries extension.\nFor 2016, The Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00227\n\n**References:**\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00227", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-npg-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean": {"abstract": "**DEFINITION**\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data inside the gyre. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\n**CONTEXT**\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\n**CMEMS KEY FINDINGS**\n\nThe trend in the South Altantic gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.01% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.09% rate for the 1997-2020 trend (though within the uncertainties associated with the two estimates) and is not statistically significant (p>0.05). \nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was positive (0.73% year-1) relative to 2000-01-01 values. This is a significant increase from the trend of 0.35% year-1 for the 1997-2020 period and is statistically significant (p<0.05). The last two years of the timeseries show an increased deviation from the mean.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00228\n\n**References:**\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00228", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-sag-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "South Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean": {"abstract": "**DEFINITION**\n\nOligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).\n\n**CONTEXT**\n\nOligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth\u2019s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. \n\n**CMEMS KEY FINDINGS**\n\nThe trend in the South Pacific gyre area for the 1997 Sept \u2013 2021 December period was positive, with a 0.04% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.16% change for the 1997-2020 period, with the sign of the trend remaining unchanged and is not statistically significant (p<0.05). An underlying low frequency signal is observed with a period of approximately a decade.\nDuring the 1997 Sept \u2013 2021 December period, the trend in chlorophyll concentration was positive (0.66% year-1) in the South Pacific gyre relative to 2000-01-01 values. This rate has increased compared to the rate of 0.45% year-1 for the 1997-2020 period and remains statistically significant (p<0.05). In the last two years of the timeseries, an increase in the variation from the mean is observed.\nFor 2016, the Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00229\n\n**References:**\n\n* Aiken J, Brewin RJW, Dufois F, Polimene L, Hardman-Mountford NJ, Jackson T, Loveday B, Hoya SM, Dall\u2019Olmo G, Stephens J, et al. 2016. A synthesis of the environmental response of the North and South Atlantic sub-tropical gyres during two decades of AMT. Prog Oceanogr. doi:10.1016/j.pocean.2016.08.004.\n* McClain CR, Signorini SR, Christian JR 2004. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Top Stud Oceanogr. 51:281\u2013301. doi:10.1016/j.dsr2.2003.08.002.\n* Polovina JJ, Howell EA, Abecassis M 2008. Ocean\u2019s least productive waters are expanding. Geophys Res Lett. 35:270. doi:10.1029/2007GL031745.\n* Sathyendranath S, Pardo S, Brewin RJW. 2018. Oligotrophic gyres. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208\n* Signorini SR, Franz BA, McClain CR 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Front Mar Sci. 2. doi:10.3389/fmars.2015.00001.\n", "doi": "10.48670/moi-00229", "instrument": null, "keywords": "area-type-oligotropic-gyre,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater-for-averaged-mean,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-oligo-spg-area-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "South Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend": {"abstract": "**DEFINITION**\n\nThe trend map is derived from version 5 of the global climate-quality chlorophyll time series produced by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020) and distributed by CMEMS. The trend detection method is based on the Census-I algorithm as described by Vantrepotte et al. (2009), where the time series is decomposed as a fixed seasonal cycle plus a linear trend component plus a residual component. The linear trend is expressed in % year -1, and its level of significance (p) calculated using a t-test. Only significant trends (p < 0.05) are included. \n\n**CONTEXT**\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration is the most widely used measure of the concentration of phytoplankton present in the ocean. Drivers for chlorophyll variability range from small-scale seasonal cycles to long-term climate oscillations and, most importantly, anthropogenic climate change. Due to such diverse factors, the detection of climate signals requires a long-term time series of consistent, well-calibrated, climate-quality data record. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\n**CMEMS KEY FINDINGS**\n\nThe average global trend for the 1997-2021 period was 0.51% per year, with a maximum value of 25% per year and a minimum value of -6.1% per year. Positive trends are pronounced in the high latitudes of both northern and southern hemispheres. The significant increases in chlorophyll reported in 2016-2017 (Sathyendranath et al., 2018b) for the Atlantic and Pacific oceans at high latitudes appear to be plateauing after the 2021 extension. The negative trends shown in equatorial waters in 2020 appear to be remain consistent in 2021. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00230\n\n**References:**\n\n* Jackson, T. (2020) OC-CCI Product User Guide (PUG). ESA/ESRIN Report. D4.2PUG, 2020-10-12. Issue:v4.2. https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018b, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sathyendranath, S, Brewin, RJW, Brockmann, C, Brotas, V, Calton, B, Chuprin, A, Cipollini, P, Couto, AB, Dingle, J, Doerffer, R, Donlon, C, Dowell, M, Farman, A, Grant, M, Groom, S, Horseman, A, Jackson, T, Krasemann, H, Lavender, S, Martinez-Vicente, V, Mazeran, C, M\u00e9lin, F, Moore, TS, Mu\u0308ller, D, Regner, P, Roy, S, Steele, CJ, Steinmetz, F, Swinton, J, Taberner, M, Thompson, A, Valente, A, Zu\u0308hlke, M, Brando, VE, Feng, H, Feldman, G, Franz, BA, Frouin, R, Gould, Jr., RW, Hooker, SB, Kahru, M, Kratzer, S, Mitchell, BG, Muller-Karger, F, Sosik, HM, Voss, KJ, Werdell, J, and Platt, T (2019) An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors: 19, 4285. doi:10.3390/s19194285\n* Vantrepotte, V., M\u00e9lin, F., 2009. Temporal variability of 10-year global SeaWiFS time series of phytoplankton chlorophyll-a concentration. ICES J. Mar. Sci., 66, 1547-1556. doi: 10.1093/icesjms/fsp107.\n", "doi": "10.48670/moi-00230", "instrument": null, "keywords": "change-in-mass-concentration-of-chlorophyll-in-seawater-over-time,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-health-chl-global-oceancolour-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean": {"abstract": "**DEFINITION**\n\nThe time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend.\n\n**CONTEXT**\n\nPhytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Mediterranean Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) and El Nin\u0303o Southern Oscillation (ENSO) (Basterretxea et al. 2018, Colella et al. 2016).\n\n**KEY FINDINGS**\n\nIn the Mediterranean Sea, the trend average for the 1997-2023 period is slightly negative (-0.73\u00b10.65% per year) emphasising the results obtained from previous release (1997-2022). This result is in contrast with the analysis of Sathyendranath et al. (2018) that reveals an increasing trend in chlorophyll concentration in all the European Seas. Starting from 2010-2011, except for 2018-2019, the decrease of chlorophyll concentrations is quite evident in the deseasonalized timeseries (in green), and in the maxima of the observations (grey line), starting from 2015. This attenuation of chlorophyll values of the last decade, results in an overall negative trend for the Mediterranean Sea.\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00259\n\n**References:**\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Berthon, J.-F., Zibordi, G. (2004). Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., Santoleri, R., 2016. Mediterranean ocean colour chlorophyll trends. PLoS One 11, 1 16. https://doi.org/10.1371/journal.pone.0155756.\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. Quality Information Document. Copernicus Marine Service. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf).\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245 259. p. 42.\n* Sathyendranath, S., Pardo, S., Benincasa, M., Brando, V. E., Brewin, R. J.W., M\u00e9lin, F., Santoleri, R., 2018, 1.5. Essential Variables: Ocean Colour in Copernicus Marine Service Ocean State Report - Issue 2, Journal of Operational Oceanography, 11:sup1, 1-142, doi: 10.1080/1755876X.2018.1489208\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379 1389.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00259", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-in-seawater,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-health-chl-medsea-oceancolour-area-averaged-mean,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1997-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Chlorophyll-a time series and trend from Observations Reprocessing"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend": {"abstract": "**DEFINITION**\n\nThis product includes the Mediterranean Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) (at 1 km resolution) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). \nThe trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens\u2019s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included.\n\n**CONTEXT**\n\nPhytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). The Mediterranean Sea is an oligotrophic basin, where chlorophyll concentration decreases following a specific gradient from West to East (Colella et al. 2016). The highest concentrations are observed in coastal areas and at the river mouths, where the anthropogenic pressure and nutrient loads impact on the eutrophication regimes (Colella et al. 2016). The the use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series.\n\n**KEY FINDINGS**\n\nChlorophyll trend in the Mediterranean Sea, for the period 1997-2023, generally confirm trend results of the previous release with negative values over most of the basin. In Ligurian Sea, negative trend is slightly emphasized. As for the previous release, the southern part of the western Mediterranean basin, Rhode Gyre and in the northern coast of the Aegean Sea show weak positive trend areas but they seems weaker than previous ones. On average the trend in the Mediterranean Sea is about -0.83% per year, emphasizing the mean negative trend achieved in the previous release. Contrary to what shown by Salgado-Hernanz et al. (2019) in their analysis (related to 1998-2014 satellite observations), western and eastern part of the Mediterranean Sea do not show differences. In the Ligurian Sea, the trend switch to negative values, differing from the positive regime observed in the trend maps of both Colella et al. (2016) and Salgado-Hernanz et al. (2019), referred, respectively, to 1998-2009 and 1998-2014 period, respectively. The waters offshore the Po River mouth show weak negative trend values, partially differing from the markable negative regime observed in the 1998-2009 period (Colella et al., 2016), and definitely moving from the positive trend observed by Salgado-Hernanz et al. (2019).\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00260\n\n**References:**\n\n* Basterretxea, G., Font-Mu\u00f1oz, J. S., Salgado-Hernanz, P. M., Arrieta, J., & Hern\u00e1ndez-Carrasco, I. (2018). Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sensing of Environment, 215, 7-17.\n* Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 200.\n* Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., & Santoleri, R. (2016). Mediterranean ocean colour chlorophyll trends. PloS one, 11(6).\n* Colella, S., Brando, V.E., Cicco, A.D., D\u2019Alimonte, D., Forneris, V., Bracaglia, M., 2021. OCEAN COLOUR PRODUCTION CENTRE, Ocean Colour Mediterranean and Black Sea Observation Product. Copernicus Marine Environment Monitoring Centre. Quality Information Document (https://documentation.marine.copernicus.eu/QUID/CMEMS-OC-QUID-009-141to144-151to154.pdf).\n* Kendall MG. 1975. Multivariate analysis. London: Charles Griffin & Co; p. 210, 43.\n* Mann HB. 1945. Nonparametric tests against trend. Econometrica. 13:245\u2013259. p. 42.\n* Pezzulli S, Stephenson DB, Hannachi A. 2005. The Variability of Seasonality. J. Climate. 18:71\u201388. doi:10.1175/JCLI-3256.1.\n* Salgado-Hernanz, P. M., Racault, M. F., Font-Mu\u00f1oz, J. S., & Basterretxea, G. (2019). Trends in phytoplankton phenology in the Mediterranean Sea based on ocean-colour remote sensing. Remote Sensing of Environment, 221, 50-64.\n* Sen PK. 1968. Estimates of the regression coefficient based on Kendall\u2019s tau. J Am Statist Assoc. 63:1379\u20131389.\n* Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146.\n", "doi": "10.48670/moi-00260", "instrument": null, "keywords": "change-in-mass-concentration-of-chlorophyll-in-seawater-over-time,coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,omi-health-chl-medsea-oceancolour-trend,satellite-observation,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea Chlorophyll-a trend map from Observations Reprocessing"}, "OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean": {"abstract": "**DEFINITION**\n\nThe Mediterranean water mass formation rates are evaluated in 4 areas as defined in the Ocean State Report issue 2 section 3.4 (Simoncelli and Pinardi, 2018) as shown in Figure 2: (1) the Gulf of Lions for the Western Mediterranean Deep Waters (WMDW); (2) the Southern Adriatic Sea Pit for the Eastern Mediterranean Deep Waters (EMDW); (3) the Cretan Sea for Cretan Intermediate Waters (CIW) and Cretan Deep Waters (CDW); (4) the Rhodes Gyre, the area of formation of the so-called Levantine Intermediate Waters (LIW) and Levantine Deep Waters (LDW).\nAnnual water mass formation rates have been computed using daily mixed layer depth estimates (density criteria \u0394\u03c3 = 0.01 kg/m3, 10 m reference level) considering the annual maximum volume of water above mixed layer depth with potential density within or higher the specific thresholds specified in Table 1 then divided by seconds per year.\nAnnual mean values are provided using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al. 2020, 2021).\n\n**CONTEXT**\n\nThe formation of intermediate and deep water masses is one of the most important processes occurring in the Mediterranean Sea, being a component of its general overturning circulation. This circulation varies at interannual and multidecadal time scales and it is composed of an upper zonal cell (Zonal Overturning Circulation) and two main meridional cells in the western and eastern Mediterranean (Pinardi and Masetti 2000).\nThe objective is to monitor the main water mass formation events using the eddy resolving Mediterranean Sea Reanalysis (Escudier et al. 2020, 2021) and considering Pinardi et al. (2015) and Simoncelli and Pinardi (2018) as references for the methodology. The Mediterranean Sea Reanalysis can reproduce both Eastern Mediterranean Transient and Western Mediterranean Transition phenomena and catches the principal water mass formation events reported in the literature. This will permit constant monitoring of the open ocean deep convection process in the Mediterranean Sea and a better understanding of the multiple drivers of the general overturning circulation at interannual and multidecadal time scales. \nDeep and intermediate water formation events reveal themselves by a deep mixed layer depth distribution in four Mediterranean areas (Table 1 and Figure 2): Gulf of Lions, Southern Adriatic Sea Pit, Cretan Sea and Rhodes Gyre. \n\n**CMEMS KEY FINDINGS**\n\nThe Western Mediterranean Deep Water (WMDW) formation events in the Gulf of Lion appear to be larger after 1999 consistently with Schroeder et al. (2006, 2008) related to the Eastern Mediterranean Transient event. This modification of WMDW after 2005 has been called Western Mediterranean Transition. WMDW formation events are consistent with Somot et al. (2016) and the event in 2009 is also reported in Houpert et al. (2016). \nThe Eastern Mediterranean Deep Water (EMDW) formation in the Southern Adriatic Pit region displays a period of water mass formation between 1988 and 1993, in agreement with Pinardi et al. (2015), in 1996, 1999 and 2000 as documented by Manca et al. (2002). Weak deep water formation in winter 2006 is confirmed by observations in Vilibi\u0107 and \u0160anti\u0107 (2008). An intense deep water formation event is detected in 2012-2013 (Ga\u010di\u0107 et al., 2014). Last years are characterized by large events starting from 2017 (Mihanovic et al., 2021).\nCretan Intermediate Water formation rates present larger peaks between 1989 and 1993 with the ones in 1992 and 1993 composing the Eastern Mediterranean Transient phenomena. The Cretan Deep Water formed in 1992 and 1993 is characterized by the highest densities of the entire period in accordance with Velaoras et al. (2014).\nThe Levantine Deep Water formation rate in the Rhode Gyre region presents the largest values between 1992 and 1993 in agreement with Kontoyiannis et al. (1999). \n\n**Figure caption**\n\nWater mass formation rates [Sverdrup] computed in 4 regions: in the Gulf of Lion for the Western Mediterranean Deep Waters (WMDW); in the Southern Adriatic region for the Eastern Mediterranean Deep Waters (EMDW); in the Cretan Sea for the Cretan Intermediate Waters (CIW) and the Cretan Deep Waters (CDW); in the Rhode Gyre area for the Levantine Intermediate Waters (LIW) and the Levantine Deep Waters (LDW). Product used: MEDSEA_MULTIYEAR_PHY_006_004.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00318\n\n**References:**\n\n* Escudier R., Clementi E., Cipollone A., Pistoia J., Drudi M., Grandi A., Lyubartsev V., Lecci R., Aydogdu A., Delrosso D., Omar M., Masina S., Coppini G., Pinardi N. 2021. A High Resolution Reanalysis for the Mediterranean Sea. Frontiers in Earth Science, Vol.9, pp.1060, DOI:10.3389/feart.2021.702285.\n* Escudier, R., Clementi, E., Omar, M., Cipollone, A., Pistoia, J., Aydogdu, A., Drudi, M., Grandi, A., Lyubartsev, V., Lecci, R., Cret\u00ed, S., Masina, S., Coppini, G., & Pinardi, N. (2020). Mediterranean Sea Physical Reanalysis (CMEMS MED-Currents) (Version 1) set. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n* Ga\u010di\u0107, M., Civitarese, G., Kova\u010devi\u0107, V., Ursella, L., Bensi, M., Menna, M., et al. 2014. Extreme winter 2012 in the Adriatic: an example of climatic effect on the BiOS rhythm. Ocean Sci. 10, 513\u2013522. doi: 10.5194/os-10-513-2014\n* Houpert, L., de Madron, X.D., Testor, P., Bosse, A., D\u2019Ortenzio, F., Bouin, M.N., Dausse, D., Le Goff, H., Kunesch, S., Labaste, M., et al. 2016. Observations of open-ocean deep convection in the northwestern Mediterranean Sea: seasonal and inter- annual variability of mixing and deep water masses for the 2007-2013 period. J Geophys Res Oceans. 121:8139\u20138171. doi:10.1002/ 2016JC011857.\n* Kontoyiannis, H., Theocharis, A., Nittis, K. 1999. Structures and characteristics of newly formed water masses in the NW levantine during 1986, 1992, 1995. In: Malanotte-Rizzoli P., Eremeev V.N., editor. The eastern Mediterranean as a laboratory basin for the assessment of contrasting ecosys- tems. NATO science series (series 2: environmental secur- ity), Vol. 51. Springer: Dordrecht.\n* Manca, B., Kovacevic, V., Gac\u030cic\u0301, M., Viezzoli, D. 2002. Dense water formation in the Southern Adriatic Sea and spreading into the Ionian Sea in the period 1997\u20131999. J Mar Sys. 33/ 34:33\u2013154.\n* Mihanovi\u0107, H., Vilibi\u0107, I., \u0160epi\u0107, J., Mati\u0107, F., Ljube\u0161i\u0107, Z., Mauri, E., Gerin, R., Notarstefano, G., Poulain, P.-M.. 2021. Observation, preconditioning and recurrence of exceptionally high salinities in the Adriatic Sea. Frontiers in Marine Science, Vol. 8, https://www.frontiersin.org/article/10.3389/fmars.2021.672210\n* Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P., ... & Bonaduce, A. 2015. Mediterranean Sea large-scale low-frequency ocean variability and water mass formation rates from 1987 to 2007: a retrospective analysis. Progress in Oceanography, 132, 318-332\n* Schroeder, K., Gasparini, G.P., Tangherlini, M., Astraldi, M. 2006. Deep and intermediate water in the western Mediterranean under the influence of the eastern Mediterranean transient. Geophys Res Lett. 33. doi:10. 1028/2006GL02712.\n* Schroeder, K., Ribotti, A., Borghini, M., Sorgente, R., Perilli, A., Gasparini, G.P. 2008. An extensive western Mediterranean deep water renewal between 2004 and 2006. Geophys Res Lett. 35(18):L18605. doi:10.1029/2008GL035146.\n* Simoncelli, S. and Pinardi, N. 2018. Water mass formation processes in the Mediterranean sea over the past 30 years. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, s13\u2013s16, DOI: 10.1080/1755876X.2018.1489208.\n* Somot, S., Houpert, L., Sevault, F., Testor, P., Bosse, A., Taupier-Letage, I., Bouin, M.N., Waldman, R., Cassou, C., Sanchez-Gomez, E., et al. 2016. Characterizing, modelling and under- standing the climate variability of the deep water formation in the North-Western Mediterranean Sea. Clim Dyn. 1\u201332. doi:10.1007/s00382-016-3295-0.\n* Velaoras, D., Krokos, G., Nittis, K., Theocharis, A. 2014. Dense intermediate water outflow from the Cretan Sea: a salinity driven, recurrent phenomenon, connected to thermohaline circulation changes. J Geophys Res Oceans. 119:4797\u20134820. doi:10.1002/2014JC009937.\n* Vilibic\u0301, I., S\u030cantic\u0301, D. 2008. Deep water ventilation traced by Synechococcus cyanobacteria. Ocean Dyn 58:119\u2013125. doi:10.1007/s10236-008-0135-8.\n* Von Schuckmann K. et al. (2018) Copernicus Marine Service Ocean State Report, Journal of Operational Oceanography, 11:sup1, S1-S142, DOI: 10.1080/1755876X.2018.1489208\n", "doi": "10.48670/mds-00318", "instrument": null, "keywords": "coastal-marine-environment,in-situ-ts-profiles,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,omi-var-extreme-wmf-medsea-area-averaged-mean,sea-level,water-mass-formation-rate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1987-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "providers": [{"name": "CMCC (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Water Mass Formation Rates from Reanalysis"}, "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012": {"abstract": "For the Antarctic Sea - A sea ice concentration product based on satellite SAR imagery and microwave radiometer data: The algorithm uses SENTINEL-1 SAR EW and IW mode dual-polarized HH/HV data combined with AMSR2 radiometer data.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00320", "doi": "10.48670/mds-00320", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-concentration,sea-ice-edge,seaice-ant-phy-auto-l3-nrt-011-012,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Ocean - High Resolution Sea Ice Information"}, "SEAICE_ANT_PHY_L3_MY_011_018": {"abstract": "Antarctic sea ice displacement during winter from medium resolution sensors since 2002\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00120", "doi": "10.48670/moi-00120", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,oceanographic-geographical-features,satellite-observation,seaice-ant-phy-l3-my-011-018,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-04-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023": {"abstract": "Arctic L3 sea ice product providing concentration, stage-of-development and floe size information retrieved from Sentinel-1 SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm and delivered on a 0.5 km grid.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00343", "doi": "10.48670/mds-00343", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,floe-size;,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-concentration,seaice-arc-phy-auto-l3-mynrt-011-023,stage-of-development,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - High Resolution Sea Ice Information L3"}, "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024": {"abstract": "Arctic L4 sea ice concentration product based on a L3 sea ice concentration product retrieved from Sentinel-1 SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm (SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023), gap-filled with OSI SAF EUMETSAT sea ice concentration products and delivered on a 1 km grid. \n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00344", "doi": "10.48670/mds-00344", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-concentration,seaice-arc-phy-auto-l4-mynrt-011-024,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - High Resolution Sea Ice Information L4"}, "SEAICE_ARC_PHY_AUTO_L4_NRT_011_015": {"abstract": "For the European Arctic Sea - A sea ice concentration product based on SAR data and microwave radiometer. The algorithm uses SENTINEL-1 SAR EW mode dual-polarized HH/HV data combined with AMSR2 radiometer data. A sea ice type product covering the same area is produced from SENTINEL-1 SAR EW mode dual-polarized HH/HV data.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00122", "doi": "10.48670/moi-00122", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,seaice-arc-phy-auto-l4-nrt-011-015,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - High resolution Sea Ice Concentration and Sea Ice Type"}, "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021": {"abstract": "Arctic Sea and Ice surface temperature\n**Detailed description:** Arctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily supercollated field using all available sensors with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00315", "doi": "10.48670/moi-00315", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,seaice-arc-phy-climate-l3-my-011-021,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016": {"abstract": "Arctic Sea and Ice surface temperature\n\n**Detailed description:**\nArctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00123", "doi": "10.48670/moi-00123", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,seaice-arc-phy-climate-l4-my-011-016,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "SEAICE_ARC_PHY_L3M_NRT_011_017": {"abstract": "For the Arctic Ocean - multiple Sentinel-1 scenes, Sigma0 calibrated and noise-corrected, with individual geographical map projections over Svalbard and Greenland Sea regions.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00124", "doi": "10.48670/moi-00124", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,seaice-arc-phy-l3m-nrt-011-017,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ARCTIC Ocean and Sea-Ice Sigma-Nought"}, "SEAICE_ARC_PHY_L4_NRT_011_014": {"abstract": "Arctic sea ice thickness from merged SMOS and Cryosat-2 (CS2) observations during freezing season between October and April. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. On the other hand, CS2 uses radar altimetry to measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00125", "doi": "10.48670/moi-00125", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-arc-phy-l4-nrt-011-014,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "FMI (Finland)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Sea Ice Thickness derived from merging CryoSat-2 and SMOS ice thickness"}, "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010": {"abstract": "Arctic sea ice drift dataset at 3, 6 and 30 day lag during winter. The Arctic low resolution sea ice drift products provided from IFREMER have a 62.5 km grid resolution. They are delivered as daily products at 3, 6 and 30 days for the cold season extended at fall and spring: from September until May, it is updated on a monthly basis. The data are Merged product from radiometer and scatterometer:\n* SSM/I 85 GHz V & H Merged product (1992-1999)\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00126", "doi": "10.48670/moi-00126", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-arc-seaice-l3-rep-observations-011-010,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1991-12-03T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002": {"abstract": "For the Arctic Ocean - The operational sea ice services at MET Norway and DMI provides ice charts of the Arctic area covering Baffin Bay, Greenland Sea, Fram Strait and Barents Sea. The charts show the ice concentration in WMO defined concentration intervals. The three different types of ice charts (datasets) are produced from twice to several times a week: MET charts are produced every weekday. DMI regional charts are produced at irregular intervals daily and a supplemental DMI overview chart is produced twice weekly.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00128", "doi": "10.48670/moi-00128", "instrument": null, "keywords": "arctic-ocean,ca,cb,cc,cd,cf,cn,coastal-marine-environment,concentration-range,ct,data-quality,fa,fb,fc,ice-poly-id-grid,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,polygon-id,polygon-type,sa,satellite-observation,sb,sc,sea-ice-area-fraction,seaice-arc-seaice-l4-nrt-observations-011-002,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea Ice Concentration Charts - Svalbard and Greenland"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007": {"abstract": "The iceberg product contains 4 datasets (IW and EW modes and mosaic for the two modes) describing iceberg concentration as number of icebergs counted within 10x10 km grid cells. The iceberg concentration is derived by applying a Constant False Alarm Rate (CFAR) algorithm on data from Synthetic Aperture Radar (SAR) satellite sensors.\n\nThe iceberg product also contains two additional datasets of individual iceberg positions in Greenland-Newfoundland-Labrador Waters. These datasets are in shapefile format to allow the best representation of the icebergs (the 1st dataset contains the iceberg point observations, the 2nd dataset contains the polygonized satellite coverage). These are also derived by applying a Constant False Alarm Rate (CFAR) algorithm on Sentinel-1 SAR imagery.\nDespite its precision (individual icebergs are proposed), this product is a generic and automated product and needs expertise to be correctly used. For all applications concerning marine navigation, please refer to the national Ice Service of the country concerned.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00129", "doi": "10.48670/moi-00129", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,number-of-icebergs-per-unit-area,oceanographic-geographical-features,satellite-observation,seaice-arc-seaice-l4-nrt-observations-011-007,target-application#seaiceforecastingapplication,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "SAR Sea Ice Berg Concentration and Individual Icebergs Observed with Sentinel-1"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008": {"abstract": "Arctic Sea and Ice surface temperature product based upon observations from the Metop_A AVHRR instrument. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00130", "doi": "10.48670/moi-00130", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,seaice-arc-seaice-l4-nrt-observations-011-008,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea and Ice Surface Temperature"}, "SEAICE_BAL_PHY_L4_MY_011_019": {"abstract": "Gridded sea ice concentration, sea ice extent and classification based on the digitized Baltic ice charts produced by the FMI/SMHI ice analysts. It is produced daily in the afternoon, describing the ice situation daily at 14:00 EET. The nominal resolution is about 1km. The temporal coverage is from the beginning of the season 1980-1981 until today.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00131", "doi": "10.48670/moi-00131", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-classification,sea-ice-concentration,sea-ice-extent,sea-ice-thickness,seaice-bal-phy-l4-my-011-019,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1980-11-03T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea ice concentration, extent, and classification time series"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004": {"abstract": "For the Baltic Sea- The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The parameters are based on ice chart produced on daily basis during the Baltic Sea ice season and show the ice concentration in a 1 km grid. Ice thickness chart (ITC) is a product based on the most recent available ice chart (IC) and a SAR image. The SAR data is used to update the ice information in the IC. The ice regions in the IC are updated according to a SAR segmentation and new ice thickness values are assigned to each SAR segment based on the SAR backscattering and the ice IC thickness range at that location.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00132\n\n**References:**\n\n* J. Karvonen, M. Simila, SAR-Based Estimation of the Baltic Sea Ice Motion, Proc. of the International Geoscience and Remote Sensing Symposium 2007 (IGARSS 07), pp. 2605-2608, 2007. (Unfortunately there is no publication of the new algorithm version yet).\n", "doi": "10.48670/moi-00132", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-extent,sea-ice-thickness,seaice-bal-seaice-l4-nrt-observations-011-004,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011": {"abstract": "For the Baltic Sea - The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The products are based on SAR images and are produced on pass-by-pass basis during the Baltic Sea ice season, and show the ice thickness and drift in a 500 m and 800m grid, respectively. The Baltic sea ice concentration product is based on data from SAR and microwave radiometer. The algorithm uses SENTINEL-1 SAR EW mode dual-polarized HH/HV data combined with AMSR2 radiometer data.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00133\n\n**References:**\n\n* J. Karvonen, Operational SAR-based sea ice drift monitoring over the Baltic Sea, Ocean Science, v. 8, pp. 473-483, (http://www.ocean-sci.net/8/473/2012/os-8-473-2012.html) 2012.\n", "doi": "10.48670/moi-00133", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-thickness,sea-ice-x-displacement,sea-ice-y-displacement,seaice-bal-seaice-l4-nrt-observations-011-011,target-application#seaiceclimate,target-application#seaiceforecastingapplication,target-application#seaiceinformation,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - SAR Sea Ice Thickness and Drift, Multisensor Sea Ice Concentration"}, "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013": {"abstract": "Arctic sea ice L3 data in separate monthly files. The time series is based on reprocessed radar altimeter satellite data from Envisat and CryoSat and is available in the freezing season between October and April. The product is brokered from the Copernicus Climate Change Service (C3S).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00127", "doi": "10.48670/moi-00127", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-glo-phy-climate-l3-my-011-013,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1995-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Arctic Ocean - Sea Ice Thickness REPROCESSED"}, "SEAICE_GLO_PHY_L4_MY_011_020": {"abstract": "The product contains a reprocessed multi year version of the daily composite dataset from SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006 covering the Sentinel1 years from autumn 2014 until 1 year before present\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00328", "doi": "10.48670/mds-00328", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-x-displacement,sea-ice-y-displacement,seaice-glo-phy-l4-my-011-020,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - High Resolution SAR Sea Ice Drift Time Series"}, "SEAICE_GLO_PHY_L4_NRT_011_014": {"abstract": "Arctic sea ice thickness from merged L-Band radiometer (SMOS ) and radar altimeter (CryoSat-2, Sentinel-3A/B) observations during freezing season between October and April in the northern hemisphere and Aprilt to October in the southern hemisphere. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. Radar altimeters measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00125", "doi": "10.48670/moi-00125", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,seaice-glo-phy-l4-nrt-011-014,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-10-18T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Sea Ice Thickness derived from merging of L-Band radiometry and radar altimeter derived sea ice thickness"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001": {"abstract": "For the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers five global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401, OSI-402, OSI-403, OSI-405 and OSI-408). The sea ice concentration, edge and type products are delivered daily at 10km resolution and the sea ice drift in 62.5km resolution, all in polar stereographic projections covering the Northern Hemisphere and the Southern Hemisphere. The sea ice drift motion vectors have a time-span of 2 days. These are the Sea Ice operational nominal products for the Global Ocean.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00134", "doi": "10.48670/moi-00134", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,sea-ice-x-displacement,sea-ice-y-displacement,seaice-glo-seaice-l4-nrt-observations-011-001,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2024-10-14T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006": {"abstract": "DTU Space produces polar covering Near Real Time gridded ice displacement fields obtained by MCC processing of Sentinel-1 SAR, Envisat ASAR WSM swath data or RADARSAT ScanSAR Wide mode data . The nominal temporal span between processed swaths is 24hours, the nominal product grid resolution is a 10km.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00135", "doi": "10.48670/moi-00135", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-x-displacement,sea-ice-y-displacement,seaice-glo-seaice-l4-nrt-observations-011-006,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean - High Resolution SAR Sea Ice Drift"}, "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009": {"abstract": "The CDR and ICDR sea ice concentration dataset of the EUMETSAT OSI SAF (OSI-450-a and OSI-430-a), covering the period from October 1978 to present, with 16 days delay. It used passive microwave data from SMMR, SSM/I and SSMIS. Sea ice concentration is computed from atmospherically corrected PMW brightness temperatures, using a combination of state-of-the-art algorithms and dynamic tie points. It includes error bars for each grid cell (uncertainties). This version 3.0 of the CDR (OSI-450-a, 1978-2020) and ICDR (OSI-430-a, 2021-present with 16 days latency) was released in November 2022\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00136\n\n**References:**\n\n* [http://osisaf.met.no/docs/osisaf_cdop2_ss2_pum_sea-ice-conc-reproc_v2p2.pdf]\n", "doi": "10.48670/moi-00136", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,seaice-glo-seaice-l4-rep-observations-011-009,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1978-10-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)"}, "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067": {"abstract": "The Mean Dynamic Topography MDT-CMEMS_2020_BLK is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Black Sea. This is consistent with the reference time period also used in the SSALTO DUACS products\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00138", "doi": "10.48670/moi-00138", "instrument": null, "keywords": "black-sea,coastal-marine-environment,invariant,level-4,marine-resources,marine-safety,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-blk-phy-mdt-l4-static-008-067,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "BLACK SEA MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_EUR_PHY_L3_MY_008_061": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n\u201c\u2019Associated products\u201d\u2019\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00139", "doi": "10.48670/moi-00139", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l3-my-008-061,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-10-03T07:53:03Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "SEALEVEL_EUR_PHY_L3_NRT_008_059": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) and 5Hz (~1km) sampling. It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Seas. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n**Associated products**\n\nA time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 [](http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033) describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00140", "doi": "10.48670/moi-00140", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l3-nrt-008-059,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T03:04:52Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA LEVEL ANOMALIES NRT"}, "SEALEVEL_EUR_PHY_L4_MY_008_068": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00141", "doi": "10.48670/moi-00141", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l4-my-008-068,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "SEALEVEL_EUR_PHY_L4_NRT_008_060": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00142", "doi": "10.48670/moi-00142", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-eur-phy-l4-nrt-008-060,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070": {"abstract": "The Mean Dynamic Topography MDT-CMEMS_2024_EUR is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the European Seas. This is consistent with the reference time period also used in the SSALTO DUACS products\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00337", "doi": "10.48670/mds-00337", "instrument": null, "keywords": "coastal-marine-environment,invariant,level-4,marine-resources,marine-safety,mediterranean-sea,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-eur-phy-mdt-l4-static-008-070,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "EUROPEAN SEAS MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057": {"abstract": "DUACS delayed-time altimeter gridded maps of sea surface heights and derived variables over the global Ocean (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global?tab=overview). The processing focuses on the stability and homogeneity of the sea level record (based on a stable two-satellite constellation) and the product is dedicated to the monitoring of the sea level long-term evolution for climate applications and the analysis of Ocean/Climate indicators. These products are produced and distributed by the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/).\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00145", "doi": "10.48670/moi-00145", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-sea-level,sealevel-glo-phy-climate-l4-my-008-057,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "SEALEVEL_GLO_PHY_L3_MY_008_062": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc.). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n**Associated products**\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n**DOI (product)**:\nhttps://doi.org/10.48670/moi-00146", "doi": "10.48670/moi-00146", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l3-my-008-062,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1992-10-03T01:42:25Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "SEALEVEL_GLO_PHY_L3_NRT_008_044": {"abstract": "Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) and 5Hz (~1km) sampling. It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n**Associated products**\nA time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 [](http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033) describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n**DOI (product)**:\nhttps://doi.org/10.48670/moi-00147", "doi": "10.48670/moi-00147", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l3-nrt-008-044,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS NRT"}, "SEALEVEL_GLO_PHY_L4_MY_008_047": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00148", "doi": "10.48670/moi-00148", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l4-my-008-047,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1993-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "SEALEVEL_GLO_PHY_L4_NRT_008_046": {"abstract": "Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [](http://duacs.cls.fr) pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00149", "doi": "10.48670/moi-00149", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sealevel-glo-phy-l4-nrt-008-046,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "SEALEVEL_GLO_PHY_MDT_008_063": {"abstract": "Mean Dynamic Topography that combines the global CNES-CLS-2022 MDT, the Black Sea CMEMS2020 MDT and the Med Sea CMEMS2020 MDT. It is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid. This is consistent with the reference time period also used in the DUACS products\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00150", "doi": "10.48670/moi-00150", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,invariant,level-4,marine-resources,marine-safety,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-glo-phy-mdt-008-063,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN MEAN DYNAMIC TOPOGRAPHY"}, "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033": {"abstract": "In wavenumber spectra, the 1hz measurement error is the noise level estimated as the mean value of energy at high wavenumbers (below ~20km in term of wavelength). The 1hz noise level spatial distribution follows the instrumental white-noise linked to the Surface Wave Height but also connections with the backscatter coefficient. The full understanding of this hump of spectral energy (Dibarboure et al., 2013, Investigating short wavelength correlated errors on low-resolution mode altimetry, OSTST 2013 presentation) still remain to be achieved and overcome with new retracking, new editing strategy or new technology.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00144", "doi": "10.48670/moi-00144", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,invariant,level-4,marine-resources,marine-safety,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-sea-level,sealevel-glo-phy-noise-l4-static-008-033,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN GRIDDED NORMALIZED MEASUREMENT NOISE OF SEA LEVEL ANOMALIES"}, "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066": {"abstract": "The Mean Dynamic Topography MDT-CMEMS_2020_MED is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Mediterranean Sea. This is consistent with the reference time period also used in the SSALTO DUACS products\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00151", "doi": "10.48670/moi-00151", "instrument": null, "keywords": "coastal-marine-environment,invariant,level-4,marine-resources,marine-safety,mediterranean-sea,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sealevel-med-phy-mdt-l4-static-008-066,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "MEDITERRANEAN SEA MEAN DYNAMIC TOPOGRAPHY"}, "SST_ATL_PHY_L3S_MY_010_038": {"abstract": "For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00311", "doi": "10.48670/moi-00311", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sst-atl-phy-l3s-my-010-038,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Reprocessed"}, "SST_ATL_PHY_L3S_NRT_010_037": {"abstract": "For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02\u00b0 resolution grid. It includes observations by polar orbiting and geostationary satellites . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. 3 more datasets are available that only contain \"per sensor type\" data: Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00310", "doi": "10.48670/moi-00310", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-atl-phy-l3s-nrt-010-037,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-20T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025": {"abstract": "For the Atlantic European North West Shelf Ocean-European North West Shelf/Iberia Biscay Irish Seas. The ODYSSEA NW+IBI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg x 0.02deg horizontal resolution, using satellite data from both infra-red and micro-wave radiometers. It is the sea surface temperature operational nominal product for the Northwest Shelf Sea and Iberia Biscay Irish Seas.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00152", "doi": "10.48670/moi-00152", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-atl-sst-l4-nrt-observations-010-025,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA L4 Sea Surface Temperature Analysis"}, "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026": {"abstract": "For the European North West Shelf Ocean Iberia Biscay Irish Seas. The IFREMER Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.05deg. x 0.05deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00153", "doi": "10.48670/moi-00153", "instrument": null, "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-atl-sst-l4-rep-observations-010-026,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "European North West Shelf/Iberia Biscay Irish Seas - High Resolution L4 Sea Surface Temperature Reprocessed"}, "SST_BAL_PHY_L3S_MY_010_040": {"abstract": "For the Baltic Sea- the DMI Sea Surface Temperature reprocessed L3S aims at providing daily multi-sensor supercollated data at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00312\n\n**References:**\n\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00312", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-phy-l3s-my-010-040,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - L3S Sea Surface Temperature Reprocessed"}, "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034": {"abstract": "For the Baltic Sea - the DMI Sea Surface Temperature Diurnal Subskin L4 aims at providing hourly analysis of the diurnal subskin signal at 0.02deg. x 0.02deg. horizontal resolution, using the BAL L4 NRT product as foundation temperature and satellite data from infra-red radiometers. Uses SST satellite products from the sensors: Metop B AVHRR, Sentinel-3 A/B SLSTR, VIIRS SUOMI NPP & NOAA20 \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00309\n\n**References:**\n\n* Karagali I. and H\u00f8yer, J. L. (2014). Characterisation and quantification of regional diurnal cycles from SEVIRI. Ocean Science, 10 (5), 745-758.\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00309", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-phy-subskin-l4-nrt-010-034,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2022-05-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032": {"abstract": "For the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00154\n\n**References:**\n\n* H\u00f8yer, J. L., Le Borgne, P. and Eastwood, S. 2014. A bias correction method for Arctic satellite sea surface temperature observations, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2013.04.020.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00154", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-sst-l3s-nrt-observations-010-032,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-03-11T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "North Sea/Baltic Sea - Sea Surface Temperature Analysis L3S"}, "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b": {"abstract": "For the Baltic Sea- The DMI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red and microwave radiometers. Uses SST nighttime satellite products from these sensors: NOAA AVHRR, Metop AVHRR, Terra MODIS, Aqua MODIS, Aqua AMSR-E, Envisat AATSR, MSG Seviri\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00155", "doi": "10.48670/moi-00155", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bal-sst-l4-nrt-observations-010-007-b,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea- Sea Surface Temperature Analysis L4"}, "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016": {"abstract": "For the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. The product uses SST satellite products from the ESA CCI and Copernicus C3S projects, including the sensors: NOAA AVHRRs 7, 9, 11, 12, 14, 15, 16, 17, 18 , 19, Metop, ATSR1, ATSR2, AATSR and SLSTR.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00156\n\n**References:**\n\n* H\u00f8yer, J. L., & Karagali, I. (2016). Sea surface temperature climate data record for the North Sea and Baltic Sea. Journal of Climate, 29(7), 2529-2541.\n* H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.H\u00f8yer, J. L. and She, J., Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea, J. Mar. Sys., Vol 65, 1-4, pp., 2007.\n", "doi": "10.48670/moi-00156", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,sst-bal-sst-l4-rep-observations-010-016,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Baltic Sea- Sea Surface Temperature Reprocessed"}, "SST_BS_PHY_L3S_MY_010_041": {"abstract": "The Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00313\n\n**References:**\n\n* Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Eastwood, S. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 1-18. Pisano, A., Buongiorno Nardelli, B., Tronconi, C. & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sens. Environ. 176, 107\u2013116.\n* Saha, Korak; Zhao, Xuepeng; Zhang, Huai-min; Casey, Kenneth S.; Zhang, Dexin; Baker-Yeboah, Sheekela; Kilpatrick, Katherine A.; Evans, Robert H.; Ryan, Thomas; Relph, John M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v52j68xx\n", "doi": "10.48670/moi-00313", "instrument": null, "keywords": "adjusted-sea-surface-temperature,black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sst-bs-phy-l3s-my-010-041,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "SST_BS_PHY_SUBSKIN_L4_NRT_010_035": {"abstract": "For the Black Sea - the CNR diurnal sub-skin Sea Surface Temperature product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Black Sea (BS) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS BS Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00157\n\n**References:**\n\n* Marullo, S., Santoleri, R., Ciani, D., Le Borgne, P., P\u00e9r\u00e9, S., Pinardi, N., ... & Nardone, G. (2014). Combining model and geostationary satellite data to reconstruct hourly SST field over the Mediterranean Sea. Remote sensing of environment, 146, 11-23.\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00157", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,sst-bs-phy-subskin-l4-nrt-010-035,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013": {"abstract": "For the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Black Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00158\n\n**References:**\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00158", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-bs-sst-l3s-nrt-observations-010-013,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006": {"abstract": "For the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain providess daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Black Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00159\n\n**References:**\n\n* Buongiorno Nardelli B., S. Colella, R. Santoleri, M. Guarracino, A. Kholod, 2009: A re-analysis of Black Sea Surface Temperature, J. Mar. Sys.., doi:10.1016/j.jmarsys.2009.07.001\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00159", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bs-sst-l4-nrt-observations-010-006,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "SST_BS_SST_L4_REP_OBSERVATIONS_010_022": {"abstract": "The Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The BS-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00160\n\n**References:**\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "doi": "10.48670/moi-00160", "instrument": null, "keywords": "black-sea,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-bs-sst-l4-rep-observations-010-022,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Black Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "SST_GLO_PHY_L3S_MY_010_039": {"abstract": "For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. \n\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00329", "doi": "10.48670/mds-00329", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-glo-phy-l3s-my-010-039,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1982-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_GLO_PHY_L4_MY_010_044": {"abstract": "For the global ocean. The IFREMER/ODYSSEA Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.10deg. x 0.10deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00345", "doi": "10.48670/mds-00345", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-glo-phy-l4-my-010-044,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean ODYSSEA L4 Sea Surface Temperature"}, "SST_GLO_PHY_L4_NRT_010_043": {"abstract": "This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours.\n\nWhereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed.\n\nThe processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00321", "doi": "10.48670/mds-00321", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-glo-phy-l4-nrt-010-043,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ODYSSEA Global Sea Surface Temperature Gridded Level 4 Daily Multi-Sensor Observations"}, "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010": {"abstract": "For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.1\u00b0 resolution global grid. It includes observations by polar orbiting (NOAA-18 & NOAAA-19/AVHRR, METOP-A/AVHRR, ENVISAT/AATSR, AQUA/AMSRE, TRMM/TMI) and geostationary (MSG/SEVIRI, GOES-11) satellites . The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.3 more datasets are available that only contain \"per sensor type\" data: Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00164", "doi": "10.48670/moi-00164", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-3,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-glo-sst-l3s-nrt-observations-010-010,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-12-20T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001": {"abstract": "For the Global Ocean- the OSTIA global foundation Sea Surface Temperature product provides daily gap-free maps of: Foundation Sea Surface Temperature at 0.05\u00b0 x 0.05\u00b0 horizontal grid resolution, using in-situ and satellite data from both infrared and microwave radiometers. \n\nThe Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the UK's Met Office and delivered by IFREMER PU. OSTIA uses satellite data provided by the GHRSST project together with in-situ observations to determine the sea surface temperature.\nA high resolution (1/20\u00b0 - approx. 6 km) daily analysis of sea surface temperature (SST) is produced for the global ocean and some lakes.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00165\n\n**References:**\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720. doi: 10.3390/rs12040720\n* Donlon, C.J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W., 2012, The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sensing of the Environment. doi: 10.1016/j.rse.2010.10.017 2011.\n* John D. Stark, Craig J. Donlon, Matthew J. Martin and Michael E. McCulloch, 2007, OSTIA : An operational, high resolution, real time, global sea surface temperature analysis system., Oceans 07 IEEE Aberdeen, conference proceedings. Marine challenges: coastline to deep sea. Aberdeen, Scotland.IEEE.\n", "doi": "10.48670/moi-00165", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,sst-glo-sst-l4-nrt-observations-010-001,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2007-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011": {"abstract": "The OSTIA (Good et al., 2020) global sea surface temperature reprocessed product provides daily gap-free maps of foundation sea surface temperature and ice concentration (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal grid resolution, using in-situ and satellite data. This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00168\n\n**References:**\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720, doi:10.3390/rs12040720\n", "doi": "10.48670/moi-00168", "instrument": null, "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,sst-glo-sst-l4-rep-observations-010-011,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024": {"abstract": "The ESA SST CCI and C3S global Sea Surface Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the (A)ATSRs, SLSTR and the AVHRR series of sensors (Merchant et al., 2019). The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00169\n\n**References:**\n\n* Good, S., Fiedler, E., Mao, C., Martin, M.J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720, doi:10.3390/rs12040720.\n* Lavergne, T., S\u00f8rensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkj\u00e6r, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49-78, doi:10.5194/tc-13-49-2019, 2019.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\n", "doi": "10.48670/moi-00169", "instrument": null, "keywords": "analysed-sst-uncertainty,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-water-temperature,sea-water-temperature-standard-error,sst-glo-sst-l4-rep-observations-010-024,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "SST_MED_PHY_L3S_MY_010_042": {"abstract": "The Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00314\n\n**References:**\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "doi": "10.48670/moi-00314", "instrument": null, "keywords": "adjusted-sea-surface-temperature,coastal-marine-environment,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,satellite-observation,sst-med-phy-l3s-my-010-042,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CNR (Italy)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "SST_MED_PHY_SUBSKIN_L4_NRT_010_036": {"abstract": "For the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n \n[How to cite](https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-reference-copernicus-marine-products-and-services)\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00170\n\n**References:**\n\n* Marullo, S., Santoleri, R., Ciani, D., Le Borgne, P., P\u00e9r\u00e9, S., Pinardi, N., ... & Nardone, G. (2014). Combining model and geostationary satellite data to reconstruct hourly SST field over the Mediterranean Sea. Remote sensing of environment, 146, 11-23.\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00170", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,sst-med-phy-subskin-l4-nrt-010-036,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2019-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012": {"abstract": "For the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Mediterranean Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00171\n\n**References:**\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n", "doi": "10.48670/moi-00171", "instrument": null, "keywords": "coastal-marine-environment,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,sst-med-sst-l3s-nrt-observations-010-012,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004": {"abstract": "For the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Mediterranean Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. Since November 2024, the L4 MED UHR processing chain makes use of an improved background field as initial guess for the Optimal Interpolation of this product. The improvement is obtained in terms of the effective spatial resolution via the application of a convolutional neural network (CNN). These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00172\n\n**References:**\n\n* Buongiorno Nardelli B., C.Tronconi, A. Pisano, R.Santoleri, 2013: High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project, Rem. Sens. Env., 129, 1-16, doi:10.1016/j.rse.2012.10.012.\n* Fanelli, C., Ciani, D., Pisano, A., & Buongiorno Nardelli, B. (2024). Deep Learning for Super-Resolution of Mediterranean Sea Surface Temperature Fields. EGUsphere, 2024, 1-18 (pre-print)\n", "doi": "10.48670/moi-00172", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-med-sst-l4-nrt-observations-010-004,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "SST_MED_SST_L4_REP_OBSERVATIONS_010_021": {"abstract": "The Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00173\n\n**References:**\n\n* Pisano, A., Nardelli, B. B., Tronconi, C., & Santoleri, R. (2016). The new Mediterranean optimally interpolated pathfinder AVHRR SST Dataset (1982\u20132012). Remote Sensing of Environment, 176, 107-116. doi: https://doi.org/10.1016/j.rse.2016.01.019\n* Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., H\u00f8yer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C., (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci Data 11, 326. doi: https://doi.org/10.1038/s41597-024-03147-w\n", "doi": "10.48670/moi-00173", "instrument": null, "keywords": "coastal-marine-environment,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-med-sst-l4-rep-observations-010-021,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "MET Norway", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Mediterranean Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002": {"abstract": "Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra.\nUsing linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered.\nThe dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land).\nThe integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.\nThis product is processed by the WAVE-TAC multi-mission SAR data processing system.\nIt processes near-real-time data from the following missions: SAR (Sentinel-1A and Sentinel-1B) and CFOSAT/SWIM.\nOne file is produced for each mission and is available in two formats depending on the user needs: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00178", "doi": "10.48670/moi-00178", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-fwk-l3-nrt-014-002,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L3_MY_014_006": {"abstract": "Multi-Year mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00174", "doi": "10.48670/moi-00174", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l3-my-014-006,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L3_NRT_014_009": {"abstract": "Near Real-Time mono-mission satellite-based 2D full wave spectral product. These very complete products enable to characterise spectrally the direction, wave length and multiple sea Sates along CFOSAT track (in boxes of 70km/90km left and right from the nadir pointing). The data format are 2D directionnal matrices. They also include integrated parameters (Hs, direction, wavelength) from the spectrum with and without partitions. \n\n**DOI (product):** \nN/A", "doi": null, "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,global-ocean,iberian-biscay-irish-seas,level-3,mediterranean-sea,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l3-nrt-014-009,wave-spectrum", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SPC_L4_NRT_014_004": {"abstract": "Near-Real-Time multi-mission global satellite-based spectral integral parameters. Only valid data are used, based on the L3 corresponding product. Included wave parameters are partition significant wave height, partition peak period and partition peak or principal direction. Those parameters are propagated in space and time at a 3-hour timestep and on a regular space grid, providing information of the swell propagation characteristics, from source to land. One file gathers one swell system, gathering observations originating from the same storm source. This product is processed by the WAVE-TAC multi-mission SAR data processing system to serve in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B. All the spectral parameter measurements are optimally interpolated using swell observations belonging to the same swell field. The SAR data processing system produces wave integral parameters by partition (partition significant wave height, partition peak period and partition peak or principal direction) and the associated standard deviation and density of propagated observations. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00175", "doi": "10.48670/moi-00175", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l4-nrt-014-004,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2021-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L4 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L3_MY_014_005": {"abstract": "Multi-Year mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle\u2026). All the missions are homogenized with respect to a reference mission and in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.\n\nThis product is based on the ESA Sea State Climate Change Initiative data Level 3 product (version 2) and is formatted by the WAVE-TAC to be homogeneous with the CMEMS Level 3 Near-real-time product. It is based on the reprocessing of GDR data from the following altimeter missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa and Jason-3. CFOSAT Multi-Year dataset is based on the reprocessing of CFOSAT Level-2P products (CNES/CLS), inter-calibrated on Jason-3 reference mission issued from the CCI Sea State dataset.\n\nOne file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH) and the unfiltered SWH (VAVH_UNFILTERED).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00176", "doi": "10.48670/moi-00176", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,wave-glo-phy-swh-l3-my-014-005,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2002-01-15T06:29:22Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L3_NRT_014_001": {"abstract": "Near-Real-Time mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle\u2026). All the missions are homogenized with respect to a reference mission (Jason-3 until April 2022, Sentinel-6A afterwards) and calibrated on in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.\n\nAs a support of information to the significant wave height, wind speed measured by the altimeters is also processed and included in the files. Wind speed values are provided by upstream products (L2) for each mission and are based on different algorithms. Only valid data are included and all the missions are homogenized with respect to the reference mission.\n\nThis product is processed by the WAVE-TAC multi-mission altimeter data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes operational data (OGDR and NRT, produced in near-real-time) from the following altimeter missions: Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Cryosat-2, SARAL/AltiKa, CFOSAT ; and interim data (IGDR, 1 to 2 days delay) from Hai Yang-2B mission.\n\nOne file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH), the unfiltered SWH (VAVH_UNFILTERED) and the wind speed (wind_speed).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00179", "doi": "10.48670/moi-00179", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,wave-glo-phy-swh-l3-nrt-014-001,weather-climate-and-seasonal-forecasting,wind-speed", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L4_MY_014_007": {"abstract": "Multi-Year gridded multi-mission merged satellite significant wave height based on CMEMS Multi-Year level-3 SWH datasets itself based on the ESA Sea State Climate Change Initiative data Level 3 product (see the product WAVE_GLO_PHY_SWH_L3_MY_014_005). Only valid data are included. It merges along-track SWH data from the following missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa, Jason-3 and CFOSAT. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC on a 2\u00b0 horizontal grid ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon) on a 0.5\u00b0 horizontal grid, using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00177", "doi": "10.48670/moi-00177", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,sea-surface-wave-significant-height-daily-maximum,sea-surface-wave-significant-height-daily-mean,sea-surface-wave-significant-height-daily-number-of-observations,sea-surface-wave-significant-height-daily-standard-deviation,sea-surface-wave-significant-height-flag,sea-surface-wave-significant-height-number-of-observations,wave-glo-phy-swh-l4-my-014-007,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2002-01-15T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "WAVE_GLO_PHY_SWH_L4_NRT_014_003": {"abstract": "Near-Real-Time gridded multi-mission merged satellite significant wave height, based on CMEMS level-3 SWH datasets. Onyl valid data are included. It merges multiple along-track SWH data (Sentinel-6A,\u00a0 Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT, SWOT-nadir, HaiYang-2B and HaiYang-2C) and produces daily gridded data at a 2\u00b0 horizontal resolution. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00180", "doi": "10.48670/moi-00180", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,wave-glo-phy-swh-l4-nrt-014-003,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "WAVE_GLO_WAV_L3_SPC_NRT_OBSERVATIONS_014_002": {"abstract": "Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes near-real-time data from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00178", "doi": "10.48670/moi-00178", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-wav-l3-spc-nrt-observations-014-002,weather-climate-and-seasonal-forecasting", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "WIND_ARC_PHY_HR_L3_MY_012_105": {"abstract": "For the Arctic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00338", "doi": "10.48670/mds-00338", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-arc-phy-hr-l3-my-012-105,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Arctic Sea"}, "WIND_ARC_PHY_HR_L3_NRT_012_100": {"abstract": "For the Arctic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.'\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00330", "doi": "10.48670/mds-00330", "instrument": null, "keywords": "arctic-ocean,eastward-wind,level-3,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-arc-phy-hr-l3-nrt-012-100,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Arctic Sea"}, "WIND_ATL_PHY_HR_L3_MY_012_106": {"abstract": "For the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00339", "doi": "10.48670/mds-00339", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-atl-phy-hr-l3-my-012-106,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Atlantic Sea"}, "WIND_ATL_PHY_HR_L3_NRT_012_101": {"abstract": "For the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00331", "doi": "10.48670/mds-00331", "instrument": null, "keywords": "eastward-wind,iberian-biscay-irish-seas,level-3,near-real-time,north-west-shelf-seas,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-atl-phy-hr-l3-nrt-012-101,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Atlantic Sea"}, "WIND_BAL_PHY_HR_L3_MY_012_107": {"abstract": "For the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00340", "doi": "10.48670/mds-00340", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-bal-phy-hr-l3-my-012-107,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Baltic Sea"}, "WIND_BAL_PHY_HR_L3_NRT_012_102": {"abstract": "For the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00332", "doi": "10.48670/mds-00332", "instrument": null, "keywords": "baltic-sea,eastward-wind,level-3,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-bal-phy-hr-l3-nrt-012-102,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Baltic Sea"}, "WIND_BLK_PHY_HR_L3_MY_012_108": {"abstract": "For the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00341", "doi": "10.48670/mds-00341", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-blk-phy-hr-l3-my-012-108,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Black Sea"}, "WIND_BLK_PHY_HR_L3_NRT_012_103": {"abstract": "For the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00333", "doi": "10.48670/mds-00333", "instrument": null, "keywords": "black-sea,eastward-wind,level-3,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-blk-phy-hr-l3-nrt-012-103,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Black Sea"}, "WIND_GLO_PHY_CLIMATE_L4_MY_012_003": {"abstract": "For the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT, QuikSCAT SeaWinds and ERS-1 and ERS-2 SCAT satellite instruments. The applied bias corrections, the standard deviation of the differences and the number of observations used to calculate the monthly average persistent bias are included in the product.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00181", "doi": "10.48670/moi-00181", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,global-ocean,iberian-biscay-irish-seas,level-4,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,northward-wind,oceanographic-geographical-features,satellite-observation,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-glo-phy-climate-l4-my-012-003,wind-speed", "license": "proprietary", "missionStartDate": "1994-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_GLO_PHY_L3_MY_012_005": {"abstract": "For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n*0.5 degrees grid for the 50 km scatterometer L2 inputs,\n*0.25 degrees grid based on 25 km scatterometer swath observations,\n*and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. \n\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The MY L3 products follow the availability of the reprocessed EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees;\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00183", "doi": "10.48670/moi-00183", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-glo-phy-l3-my-012-005,wind-speed,wind-to-direction,wvc-index", "license": "proprietary", "missionStartDate": "1991-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "WIND_GLO_PHY_L3_NRT_012_002": {"abstract": "For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n\n*0.5 degrees grid for the 50 km scatterometer L2 inputs,\n*0.25 degrees grid based on 25 km scatterometer swath observations,\n*and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products.\n\nData from ascending and descending passes are gridded separately.\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The NRT L3 products follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for:\n*The ASCAT scatterometers on Metop-A (discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees;\n*The OSCAT scatterometer on Scatsat-1 (discontinued on 28/02/2021) and Oceansat-3 at 0.25 and 0.5 degrees; \n*The HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees\n\nIn addition, the product includes European Centre for Medium-Range Weather Forecasts (ECMWF) operational model forecast wind and stress variables collocated with the scatterometer observations at L2 and processed to L3 in exactly the same way as the scatterometer observations.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00182", "doi": "10.48670/moi-00182", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-glo-phy-l3-nrt-012-002,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": "proprietary", "missionStartDate": "2016-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "WIND_GLO_PHY_L4_MY_012_006": {"abstract": "For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 and 0.25 degrees horizontal spatial resolution. Scatterometer observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF ERA5 model fields. Bias corrections are based on scatterometer observations from Metop-A, Metop-B, Metop-C ASCAT (0.125 degrees) and QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT (0.25 degrees). The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00185", "doi": "10.48670/moi-00185", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,global-ocean,level-4,marine-resources,marine-safety,multi-year,northward-wind,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence,wind-glo-phy-l4-my-012-006", "license": "proprietary", "missionStartDate": "1994-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_GLO_PHY_L4_NRT_012_004": {"abstract": "For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00305", "doi": "10.48670/moi-00305", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,global-ocean,level-4,marine-resources,marine-safety,near-real-time,northward-wind,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence,wind-glo-phy-l4-nrt-012-004", "license": "proprietary", "missionStartDate": "2020-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 4", "providers": [{"name": "KNMI (The Netherlands)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model"}, "WIND_MED_PHY_HR_L3_MY_012_109": {"abstract": "For the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00342", "doi": "10.48670/mds-00342", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-med-phy-hr-l3-my-012-109,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "Ifremer (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from MY Satellite Measurements over the Mediterranean Sea"}, "WIND_MED_PHY_HR_L3_NRT_012_104": {"abstract": "For the Mediterranean Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00334", "doi": "10.48670/mds-00334", "instrument": null, "keywords": "eastward-wind,level-3,mediterranean-sea,near-real-time,northward-wind,oceanographic-geographical-features,quality-flag,quality-flag-wind-speed,satellite-observation,status-flag,time,wind-med-phy-hr-l3-nrt-012-104,wind-speed,wind-to-direction", "license": "proprietary", "missionStartDate": "1970-01-01T00:00:00.000000Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": "Level 3", "providers": [{"name": "CLS (France)", "roles": ["producer"]}, {"name": "Copernicus Marine Service", "roles": ["host", "processor"], "url": "https://marine.copernicus.eu"}], "title": "High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Mediterranean Sea"}}, "providers_config": {"ANTARCTIC_OMI_SI_extent": {"collection": "ANTARCTIC_OMI_SI_extent"}, "ANTARCTIC_OMI_SI_extent_obs": {"collection": "ANTARCTIC_OMI_SI_extent_obs"}, "ARCTIC_ANALYSISFORECAST_BGC_002_004": {"collection": "ARCTIC_ANALYSISFORECAST_BGC_002_004"}, "ARCTIC_ANALYSISFORECAST_PHY_002_001": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_002_001"}, "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011"}, "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015": {"collection": "ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015"}, "ARCTIC_ANALYSIS_FORECAST_WAV_002_014": {"collection": "ARCTIC_ANALYSIS_FORECAST_WAV_002_014"}, "ARCTIC_MULTIYEAR_BGC_002_005": {"collection": "ARCTIC_MULTIYEAR_BGC_002_005"}, "ARCTIC_MULTIYEAR_PHY_002_003": {"collection": "ARCTIC_MULTIYEAR_PHY_002_003"}, "ARCTIC_MULTIYEAR_PHY_ICE_002_016": {"collection": "ARCTIC_MULTIYEAR_PHY_ICE_002_016"}, "ARCTIC_MULTIYEAR_WAV_002_013": {"collection": "ARCTIC_MULTIYEAR_WAV_002_013"}, "ARCTIC_OMI_SI_Transport_NordicSeas": {"collection": "ARCTIC_OMI_SI_Transport_NordicSeas"}, "ARCTIC_OMI_SI_extent": {"collection": "ARCTIC_OMI_SI_extent"}, "ARCTIC_OMI_SI_extent_obs": {"collection": "ARCTIC_OMI_SI_extent_obs"}, "ARCTIC_OMI_TEMPSAL_FWC": {"collection": "ARCTIC_OMI_TEMPSAL_FWC"}, "BALTICSEA_ANALYSISFORECAST_BGC_003_007": {"collection": "BALTICSEA_ANALYSISFORECAST_BGC_003_007"}, "BALTICSEA_ANALYSISFORECAST_PHY_003_006": {"collection": "BALTICSEA_ANALYSISFORECAST_PHY_003_006"}, "BALTICSEA_ANALYSISFORECAST_WAV_003_010": {"collection": "BALTICSEA_ANALYSISFORECAST_WAV_003_010"}, "BALTICSEA_MULTIYEAR_BGC_003_012": {"collection": "BALTICSEA_MULTIYEAR_BGC_003_012"}, "BALTICSEA_MULTIYEAR_PHY_003_011": {"collection": "BALTICSEA_MULTIYEAR_PHY_003_011"}, "BALTICSEA_MULTIYEAR_WAV_003_015": {"collection": "BALTICSEA_MULTIYEAR_WAV_003_015"}, "BALTICSEA_REANALYSIS_WAV_003_015": {"collection": "BALTICSEA_REANALYSIS_WAV_003_015"}, "BALTIC_OMI_HEALTH_codt_volume": {"collection": "BALTIC_OMI_HEALTH_codt_volume"}, "BALTIC_OMI_OHC_area_averaged_anomalies": {"collection": "BALTIC_OMI_OHC_area_averaged_anomalies"}, "BALTIC_OMI_SI_extent": {"collection": "BALTIC_OMI_SI_extent"}, "BALTIC_OMI_SI_volume": {"collection": "BALTIC_OMI_SI_volume"}, "BALTIC_OMI_TEMPSAL_Stz_trend": {"collection": "BALTIC_OMI_TEMPSAL_Stz_trend"}, "BALTIC_OMI_TEMPSAL_Ttz_trend": {"collection": "BALTIC_OMI_TEMPSAL_Ttz_trend"}, "BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm": {"collection": "BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm"}, "BALTIC_OMI_WMHE_mbi_sto2tz_gotland": {"collection": "BALTIC_OMI_WMHE_mbi_sto2tz_gotland"}, "BLKSEA_ANALYSISFORECAST_BGC_007_010": {"collection": "BLKSEA_ANALYSISFORECAST_BGC_007_010"}, "BLKSEA_ANALYSISFORECAST_PHY_007_001": {"collection": "BLKSEA_ANALYSISFORECAST_PHY_007_001"}, "BLKSEA_ANALYSISFORECAST_WAV_007_003": {"collection": "BLKSEA_ANALYSISFORECAST_WAV_007_003"}, "BLKSEA_MULTIYEAR_BGC_007_005": {"collection": "BLKSEA_MULTIYEAR_BGC_007_005"}, "BLKSEA_MULTIYEAR_PHY_007_004": {"collection": "BLKSEA_MULTIYEAR_PHY_007_004"}, "BLKSEA_MULTIYEAR_WAV_007_006": {"collection": "BLKSEA_MULTIYEAR_WAV_007_006"}, "BLKSEA_OMI_HEALTH_oxygen_trend": {"collection": "BLKSEA_OMI_HEALTH_oxygen_trend"}, "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "BLKSEA_OMI_TEMPSAL_sst_trend": {"collection": "BLKSEA_OMI_TEMPSAL_sst_trend"}, "GLOBAL_ANALYSISFORECAST_BGC_001_028": {"collection": "GLOBAL_ANALYSISFORECAST_BGC_001_028"}, "GLOBAL_ANALYSISFORECAST_PHY_001_024": {"collection": "GLOBAL_ANALYSISFORECAST_PHY_001_024"}, "GLOBAL_ANALYSISFORECAST_WAV_001_027": {"collection": "GLOBAL_ANALYSISFORECAST_WAV_001_027"}, "GLOBAL_MULTIYEAR_BGC_001_029": {"collection": "GLOBAL_MULTIYEAR_BGC_001_029"}, "GLOBAL_MULTIYEAR_BGC_001_033": {"collection": "GLOBAL_MULTIYEAR_BGC_001_033"}, "GLOBAL_MULTIYEAR_PHY_001_030": {"collection": "GLOBAL_MULTIYEAR_PHY_001_030"}, "GLOBAL_MULTIYEAR_PHY_ENS_001_031": {"collection": "GLOBAL_MULTIYEAR_PHY_ENS_001_031"}, "GLOBAL_MULTIYEAR_WAV_001_032": {"collection": "GLOBAL_MULTIYEAR_WAV_001_032"}, "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly": {"collection": "GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly"}, "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies": {"collection": "GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies"}, "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated": {"collection": "GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated"}, "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged": {"collection": "GLOBAL_OMI_HEALTH_carbon_ph_area_averaged"}, "GLOBAL_OMI_HEALTH_carbon_ph_trend": {"collection": "GLOBAL_OMI_HEALTH_carbon_ph_trend"}, "GLOBAL_OMI_NATLANTIC_amoc_26N_profile": {"collection": "GLOBAL_OMI_NATLANTIC_amoc_26N_profile"}, "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries": {"collection": "GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000": {"collection": "GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_300": {"collection": "GLOBAL_OMI_OHC_area_averaged_anomalies_0_300"}, "GLOBAL_OMI_OHC_area_averaged_anomalies_0_700": {"collection": "GLOBAL_OMI_OHC_area_averaged_anomalies_0_700"}, "GLOBAL_OMI_OHC_trend": {"collection": "GLOBAL_OMI_OHC_trend"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000": {"collection": "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000"}, "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700": {"collection": "GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700"}, "GLOBAL_OMI_SL_thsl_trend": {"collection": "GLOBAL_OMI_SL_thsl_trend"}, "GLOBAL_OMI_TEMPSAL_Tyz_trend": {"collection": "GLOBAL_OMI_TEMPSAL_Tyz_trend"}, "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "GLOBAL_OMI_TEMPSAL_sst_trend": {"collection": "GLOBAL_OMI_TEMPSAL_sst_trend"}, "GLOBAL_OMI_WMHE_heattrp": {"collection": "GLOBAL_OMI_WMHE_heattrp"}, "GLOBAL_OMI_WMHE_northward_mht": {"collection": "GLOBAL_OMI_WMHE_northward_mht"}, "GLOBAL_OMI_WMHE_voltrp": {"collection": "GLOBAL_OMI_WMHE_voltrp"}, "IBI_ANALYSISFORECAST_BGC_005_004": {"collection": "IBI_ANALYSISFORECAST_BGC_005_004"}, "IBI_ANALYSISFORECAST_PHY_005_001": {"collection": "IBI_ANALYSISFORECAST_PHY_005_001"}, "IBI_ANALYSISFORECAST_WAV_005_005": {"collection": "IBI_ANALYSISFORECAST_WAV_005_005"}, "IBI_MULTIYEAR_BGC_005_003": {"collection": "IBI_MULTIYEAR_BGC_005_003"}, "IBI_MULTIYEAR_PHY_005_002": {"collection": "IBI_MULTIYEAR_PHY_005_002"}, "IBI_MULTIYEAR_WAV_005_006": {"collection": "IBI_MULTIYEAR_WAV_005_006"}, "IBI_OMI_CURRENTS_cui": {"collection": "IBI_OMI_CURRENTS_cui"}, "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "IBI_OMI_SEASTATE_swi": {"collection": "IBI_OMI_SEASTATE_swi"}, "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "IBI_OMI_WMHE_mow": {"collection": "IBI_OMI_WMHE_mow"}, "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031": {"collection": "INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031"}, "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032": {"collection": "INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032"}, "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034": {"collection": "INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034"}, "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050": {"collection": "INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050"}, "INSITU_GLO_BGC_DISCRETE_MY_013_046": {"collection": "INSITU_GLO_BGC_DISCRETE_MY_013_046"}, "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030": {"collection": "INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030"}, "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053": {"collection": "INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053"}, "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001": {"collection": "INSITU_GLO_PHY_TS_DISCRETE_MY_013_001"}, "INSITU_GLO_PHY_TS_OA_MY_013_052": {"collection": "INSITU_GLO_PHY_TS_OA_MY_013_052"}, "INSITU_GLO_PHY_TS_OA_NRT_013_002": {"collection": "INSITU_GLO_PHY_TS_OA_NRT_013_002"}, "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044": {"collection": "INSITU_GLO_PHY_UV_DISCRETE_MY_013_044"}, "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048": {"collection": "INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048"}, "INSITU_GLO_WAV_DISCRETE_MY_013_045": {"collection": "INSITU_GLO_WAV_DISCRETE_MY_013_045"}, "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033": {"collection": "INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033"}, "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035": {"collection": "INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035"}, "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036": {"collection": "INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036"}, "MEDSEA_ANALYSISFORECAST_BGC_006_014": {"collection": "MEDSEA_ANALYSISFORECAST_BGC_006_014"}, "MEDSEA_ANALYSISFORECAST_PHY_006_013": {"collection": "MEDSEA_ANALYSISFORECAST_PHY_006_013"}, "MEDSEA_ANALYSISFORECAST_WAV_006_017": {"collection": "MEDSEA_ANALYSISFORECAST_WAV_006_017"}, "MEDSEA_MULTIYEAR_BGC_006_008": {"collection": "MEDSEA_MULTIYEAR_BGC_006_008"}, "MEDSEA_MULTIYEAR_PHY_006_004": {"collection": "MEDSEA_MULTIYEAR_PHY_006_004"}, "MEDSEA_MULTIYEAR_WAV_006_012": {"collection": "MEDSEA_MULTIYEAR_WAV_006_012"}, "MEDSEA_OMI_OHC_area_averaged_anomalies": {"collection": "MEDSEA_OMI_OHC_area_averaged_anomalies"}, "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly": {"collection": "MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly"}, "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies": {"collection": "MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies"}, "MEDSEA_OMI_TEMPSAL_sst_trend": {"collection": "MEDSEA_OMI_TEMPSAL_sst_trend"}, "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009": {"collection": "MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009"}, "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010": {"collection": "MULTIOBS_GLO_BIO_BGC_3D_REP_015_010"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008": {"collection": "MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008"}, "MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008": {"collection": "MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008"}, "MULTIOBS_GLO_PHY_MYNRT_015_003": {"collection": "MULTIOBS_GLO_PHY_MYNRT_015_003"}, "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014": {"collection": "MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014"}, "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015": {"collection": "MULTIOBS_GLO_PHY_SSS_L4_MY_015_015"}, "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013": {"collection": "MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013"}, "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012": {"collection": "MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012"}, "MULTIOBS_GLO_PHY_W_3D_REP_015_007": {"collection": "MULTIOBS_GLO_PHY_W_3D_REP_015_007"}, "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly": {"collection": "NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly"}, "NWSHELF_ANALYSISFORECAST_BGC_004_002": {"collection": "NWSHELF_ANALYSISFORECAST_BGC_004_002"}, "NWSHELF_ANALYSISFORECAST_PHY_004_013": {"collection": "NWSHELF_ANALYSISFORECAST_PHY_004_013"}, "NWSHELF_ANALYSISFORECAST_WAV_004_014": {"collection": "NWSHELF_ANALYSISFORECAST_WAV_004_014"}, "NWSHELF_MULTIYEAR_BGC_004_011": {"collection": "NWSHELF_MULTIYEAR_BGC_004_011"}, "NWSHELF_MULTIYEAR_PHY_004_009": {"collection": "NWSHELF_MULTIYEAR_PHY_004_009"}, "NWSHELF_REANALYSIS_WAV_004_015": {"collection": "NWSHELF_REANALYSIS_WAV_004_015"}, "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201": {"collection": "OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201"}, "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207": {"collection": "OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207"}, "OCEANCOLOUR_ARC_BGC_L3_MY_009_123": {"collection": "OCEANCOLOUR_ARC_BGC_L3_MY_009_123"}, "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121": {"collection": "OCEANCOLOUR_ARC_BGC_L3_NRT_009_121"}, "OCEANCOLOUR_ARC_BGC_L4_MY_009_124": {"collection": "OCEANCOLOUR_ARC_BGC_L4_MY_009_124"}, "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122": {"collection": "OCEANCOLOUR_ARC_BGC_L4_NRT_009_122"}, "OCEANCOLOUR_ATL_BGC_L3_MY_009_113": {"collection": "OCEANCOLOUR_ATL_BGC_L3_MY_009_113"}, "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111": {"collection": "OCEANCOLOUR_ATL_BGC_L3_NRT_009_111"}, "OCEANCOLOUR_ATL_BGC_L4_MY_009_118": {"collection": "OCEANCOLOUR_ATL_BGC_L4_MY_009_118"}, "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116": {"collection": "OCEANCOLOUR_ATL_BGC_L4_NRT_009_116"}, "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202": {"collection": "OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202"}, "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208": {"collection": "OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208"}, "OCEANCOLOUR_BAL_BGC_L3_MY_009_133": {"collection": "OCEANCOLOUR_BAL_BGC_L3_MY_009_133"}, "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131": {"collection": "OCEANCOLOUR_BAL_BGC_L3_NRT_009_131"}, "OCEANCOLOUR_BAL_BGC_L4_MY_009_134": {"collection": "OCEANCOLOUR_BAL_BGC_L4_MY_009_134"}, "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132": {"collection": "OCEANCOLOUR_BAL_BGC_L4_NRT_009_132"}, "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206": {"collection": "OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206"}, "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212": {"collection": "OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212"}, "OCEANCOLOUR_BLK_BGC_L3_MY_009_153": {"collection": "OCEANCOLOUR_BLK_BGC_L3_MY_009_153"}, "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151": {"collection": "OCEANCOLOUR_BLK_BGC_L3_NRT_009_151"}, "OCEANCOLOUR_BLK_BGC_L4_MY_009_154": {"collection": "OCEANCOLOUR_BLK_BGC_L4_MY_009_154"}, "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152": {"collection": "OCEANCOLOUR_BLK_BGC_L4_NRT_009_152"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_103": {"collection": "OCEANCOLOUR_GLO_BGC_L3_MY_009_103"}, "OCEANCOLOUR_GLO_BGC_L3_MY_009_107": {"collection": "OCEANCOLOUR_GLO_BGC_L3_MY_009_107"}, "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101": {"collection": "OCEANCOLOUR_GLO_BGC_L3_NRT_009_101"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_104": {"collection": "OCEANCOLOUR_GLO_BGC_L4_MY_009_104"}, "OCEANCOLOUR_GLO_BGC_L4_MY_009_108": {"collection": "OCEANCOLOUR_GLO_BGC_L4_MY_009_108"}, "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102": {"collection": "OCEANCOLOUR_GLO_BGC_L4_NRT_009_102"}, "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204": {"collection": "OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204"}, "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210": {"collection": "OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210"}, "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205": {"collection": "OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205"}, "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211": {"collection": "OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211"}, "OCEANCOLOUR_MED_BGC_L3_MY_009_143": {"collection": "OCEANCOLOUR_MED_BGC_L3_MY_009_143"}, "OCEANCOLOUR_MED_BGC_L3_NRT_009_141": {"collection": "OCEANCOLOUR_MED_BGC_L3_NRT_009_141"}, "OCEANCOLOUR_MED_BGC_L4_MY_009_144": {"collection": "OCEANCOLOUR_MED_BGC_L4_MY_009_144"}, "OCEANCOLOUR_MED_BGC_L4_NRT_009_142": {"collection": "OCEANCOLOUR_MED_BGC_L4_NRT_009_142"}, "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203": {"collection": "OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203"}, "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209": {"collection": "OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209"}, "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index": {"collection": "OMI_CIRCULATION_BOUNDARY_BLKSEA_rim_current_index"}, "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged": {"collection": "OMI_CIRCULATION_BOUNDARY_PACIFIC_kuroshio_phase_area_averaged"}, "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean": {"collection": "OMI_CIRCULATION_MOC_BLKSEA_area_averaged_mean"}, "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean": {"collection": "OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean"}, "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged": {"collection": "OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged"}, "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies": {"collection": "OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies"}, "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OFC_BALTIC_area_averaged_anomalies"}, "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies"}, "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_OHC_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_OSC_MEDSEA_volume_mean": {"collection": "OMI_CLIMATE_OSC_MEDSEA_volume_mean"}, "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_BALTIC_area_averaged_anomalies"}, "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_BLKSEA_area_averaged_anomalies"}, "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_EUROPE_area_averaged_anomalies"}, "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_GLOBAL_area_averaged_anomalies"}, "OMI_CLIMATE_SL_GLOBAL_regional_trends": {"collection": "OMI_CLIMATE_SL_GLOBAL_regional_trends"}, "OMI_CLIMATE_SL_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_MEDSEA_area_averaged_anomalies"}, "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SL_NORTHWESTSHELF_area_averaged_anomalies"}, "OMI_CLIMATE_SST_BAL_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_BAL_area_averaged_anomalies"}, "OMI_CLIMATE_SST_BAL_trend": {"collection": "OMI_CLIMATE_SST_BAL_trend"}, "OMI_CLIMATE_SST_IBI_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_IBI_area_averaged_anomalies"}, "OMI_CLIMATE_SST_IBI_trend": {"collection": "OMI_CLIMATE_SST_IBI_trend"}, "OMI_CLIMATE_SST_IST_ARCTIC_anomaly": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_anomaly"}, "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_area_averaged_anomalies"}, "OMI_CLIMATE_SST_IST_ARCTIC_trend": {"collection": "OMI_CLIMATE_SST_IST_ARCTIC_trend"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies": {"collection": "OMI_CLIMATE_SST_NORTHWESTSHELF_area_averaged_anomalies"}, "OMI_CLIMATE_SST_NORTHWESTSHELF_trend": {"collection": "OMI_CLIMATE_SST_NORTHWESTSHELF_trend"}, "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection": {"collection": "OMI_EXTREME_CLIMVAR_PACIFIC_npgo_sla_eof_mode_projection"}, "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies": {"collection": "OMI_EXTREME_MHW_ARCTIC_area_averaged_anomalies"}, "OMI_EXTREME_SEASTATE_GLOBAL_swh_mean_and_P95_obs": {"collection": "OMI_EXTREME_SEASTATE_GLOBAL_swh_mean_and_P95_obs"}, "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_IBI_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_BLKSEA_recent_changes": {"collection": "OMI_EXTREME_WAVE_BLKSEA_recent_changes"}, "OMI_EXTREME_WAVE_BLKSEA_wave_power": {"collection": "OMI_EXTREME_WAVE_BLKSEA_wave_power"}, "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs"}, "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs": {"collection": "OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs"}, "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean"}, "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean": {"collection": "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean"}, "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend": {"collection": "OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend"}, "OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean": {"collection": "OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean"}, "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012": {"collection": "SEAICE_ANT_PHY_AUTO_L3_NRT_011_012"}, "SEAICE_ANT_PHY_L3_MY_011_018": {"collection": "SEAICE_ANT_PHY_L3_MY_011_018"}, "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023": {"collection": "SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023"}, "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024": {"collection": "SEAICE_ARC_PHY_AUTO_L4_MYNRT_011_024"}, "SEAICE_ARC_PHY_AUTO_L4_NRT_011_015": {"collection": "SEAICE_ARC_PHY_AUTO_L4_NRT_011_015"}, "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021": {"collection": "SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021"}, "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016": {"collection": "SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016"}, "SEAICE_ARC_PHY_L3M_NRT_011_017": {"collection": "SEAICE_ARC_PHY_L3M_NRT_011_017"}, "SEAICE_ARC_PHY_L4_NRT_011_014": {"collection": "SEAICE_ARC_PHY_L4_NRT_011_014"}, "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010": {"collection": "SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007"}, "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008": {"collection": "SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008"}, "SEAICE_BAL_PHY_L4_MY_011_019": {"collection": "SEAICE_BAL_PHY_L4_MY_011_019"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004": {"collection": "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004"}, "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011": {"collection": "SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011"}, "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013": {"collection": "SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013"}, "SEAICE_GLO_PHY_L4_MY_011_020": {"collection": "SEAICE_GLO_PHY_L4_MY_011_020"}, "SEAICE_GLO_PHY_L4_NRT_011_014": {"collection": "SEAICE_GLO_PHY_L4_NRT_011_014"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001": {"collection": "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001"}, "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006": {"collection": "SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006"}, "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009": {"collection": "SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009"}, "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067": {"collection": "SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067"}, "SEALEVEL_EUR_PHY_L3_MY_008_061": {"collection": "SEALEVEL_EUR_PHY_L3_MY_008_061"}, "SEALEVEL_EUR_PHY_L3_NRT_008_059": {"collection": "SEALEVEL_EUR_PHY_L3_NRT_008_059"}, "SEALEVEL_EUR_PHY_L4_MY_008_068": {"collection": "SEALEVEL_EUR_PHY_L4_MY_008_068"}, "SEALEVEL_EUR_PHY_L4_NRT_008_060": {"collection": "SEALEVEL_EUR_PHY_L4_NRT_008_060"}, "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070": {"collection": "SEALEVEL_EUR_PHY_MDT_L4_STATIC_008_070"}, "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057": {"collection": "SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057"}, "SEALEVEL_GLO_PHY_L3_MY_008_062": {"collection": "SEALEVEL_GLO_PHY_L3_MY_008_062"}, "SEALEVEL_GLO_PHY_L3_NRT_008_044": {"collection": "SEALEVEL_GLO_PHY_L3_NRT_008_044"}, "SEALEVEL_GLO_PHY_L4_MY_008_047": {"collection": "SEALEVEL_GLO_PHY_L4_MY_008_047"}, "SEALEVEL_GLO_PHY_L4_NRT_008_046": {"collection": "SEALEVEL_GLO_PHY_L4_NRT_008_046"}, "SEALEVEL_GLO_PHY_MDT_008_063": {"collection": "SEALEVEL_GLO_PHY_MDT_008_063"}, "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033": {"collection": "SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033"}, "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066": {"collection": "SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066"}, "SST_ATL_PHY_L3S_MY_010_038": {"collection": "SST_ATL_PHY_L3S_MY_010_038"}, "SST_ATL_PHY_L3S_NRT_010_037": {"collection": "SST_ATL_PHY_L3S_NRT_010_037"}, "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025": {"collection": "SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025"}, "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026": {"collection": "SST_ATL_SST_L4_REP_OBSERVATIONS_010_026"}, "SST_BAL_PHY_L3S_MY_010_040": {"collection": "SST_BAL_PHY_L3S_MY_010_040"}, "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034": {"collection": "SST_BAL_PHY_SUBSKIN_L4_NRT_010_034"}, "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032": {"collection": "SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032"}, "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b": {"collection": "SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b"}, "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016": {"collection": "SST_BAL_SST_L4_REP_OBSERVATIONS_010_016"}, "SST_BS_PHY_L3S_MY_010_041": {"collection": "SST_BS_PHY_L3S_MY_010_041"}, "SST_BS_PHY_SUBSKIN_L4_NRT_010_035": {"collection": "SST_BS_PHY_SUBSKIN_L4_NRT_010_035"}, "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013": {"collection": "SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013"}, "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006": {"collection": "SST_BS_SST_L4_NRT_OBSERVATIONS_010_006"}, "SST_BS_SST_L4_REP_OBSERVATIONS_010_022": {"collection": "SST_BS_SST_L4_REP_OBSERVATIONS_010_022"}, "SST_GLO_PHY_L3S_MY_010_039": {"collection": "SST_GLO_PHY_L3S_MY_010_039"}, "SST_GLO_PHY_L4_MY_010_044": {"collection": "SST_GLO_PHY_L4_MY_010_044"}, "SST_GLO_PHY_L4_NRT_010_043": {"collection": "SST_GLO_PHY_L4_NRT_010_043"}, "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010": {"collection": "SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010"}, "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001": {"collection": "SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011": {"collection": "SST_GLO_SST_L4_REP_OBSERVATIONS_010_011"}, "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024": {"collection": "SST_GLO_SST_L4_REP_OBSERVATIONS_010_024"}, "SST_MED_PHY_L3S_MY_010_042": {"collection": "SST_MED_PHY_L3S_MY_010_042"}, "SST_MED_PHY_SUBSKIN_L4_NRT_010_036": {"collection": "SST_MED_PHY_SUBSKIN_L4_NRT_010_036"}, "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012": {"collection": "SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012"}, "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004": {"collection": "SST_MED_SST_L4_NRT_OBSERVATIONS_010_004"}, "SST_MED_SST_L4_REP_OBSERVATIONS_010_021": {"collection": "SST_MED_SST_L4_REP_OBSERVATIONS_010_021"}, "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002": {"collection": "WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002"}, "WAVE_GLO_PHY_SPC_L3_MY_014_006": {"collection": "WAVE_GLO_PHY_SPC_L3_MY_014_006"}, "WAVE_GLO_PHY_SPC_L3_NRT_014_009": {"collection": "WAVE_GLO_PHY_SPC_L3_NRT_014_009"}, "WAVE_GLO_PHY_SPC_L4_NRT_014_004": {"collection": "WAVE_GLO_PHY_SPC_L4_NRT_014_004"}, "WAVE_GLO_PHY_SWH_L3_MY_014_005": {"collection": "WAVE_GLO_PHY_SWH_L3_MY_014_005"}, "WAVE_GLO_PHY_SWH_L3_NRT_014_001": {"collection": "WAVE_GLO_PHY_SWH_L3_NRT_014_001"}, "WAVE_GLO_PHY_SWH_L4_MY_014_007": {"collection": "WAVE_GLO_PHY_SWH_L4_MY_014_007"}, "WAVE_GLO_PHY_SWH_L4_NRT_014_003": {"collection": "WAVE_GLO_PHY_SWH_L4_NRT_014_003"}, "WAVE_GLO_WAV_L3_SPC_NRT_OBSERVATIONS_014_002": {"collection": "WAVE_GLO_WAV_L3_SPC_NRT_OBSERVATIONS_014_002"}, "WIND_ARC_PHY_HR_L3_MY_012_105": {"collection": "WIND_ARC_PHY_HR_L3_MY_012_105"}, "WIND_ARC_PHY_HR_L3_NRT_012_100": {"collection": "WIND_ARC_PHY_HR_L3_NRT_012_100"}, "WIND_ATL_PHY_HR_L3_MY_012_106": {"collection": "WIND_ATL_PHY_HR_L3_MY_012_106"}, "WIND_ATL_PHY_HR_L3_NRT_012_101": {"collection": "WIND_ATL_PHY_HR_L3_NRT_012_101"}, "WIND_BAL_PHY_HR_L3_MY_012_107": {"collection": "WIND_BAL_PHY_HR_L3_MY_012_107"}, "WIND_BAL_PHY_HR_L3_NRT_012_102": {"collection": "WIND_BAL_PHY_HR_L3_NRT_012_102"}, "WIND_BLK_PHY_HR_L3_MY_012_108": {"collection": "WIND_BLK_PHY_HR_L3_MY_012_108"}, "WIND_BLK_PHY_HR_L3_NRT_012_103": {"collection": "WIND_BLK_PHY_HR_L3_NRT_012_103"}, "WIND_GLO_PHY_CLIMATE_L4_MY_012_003": {"collection": "WIND_GLO_PHY_CLIMATE_L4_MY_012_003"}, "WIND_GLO_PHY_L3_MY_012_005": {"collection": "WIND_GLO_PHY_L3_MY_012_005"}, "WIND_GLO_PHY_L3_NRT_012_002": {"collection": "WIND_GLO_PHY_L3_NRT_012_002"}, "WIND_GLO_PHY_L4_MY_012_006": {"collection": "WIND_GLO_PHY_L4_MY_012_006"}, "WIND_GLO_PHY_L4_NRT_012_004": {"collection": "WIND_GLO_PHY_L4_NRT_012_004"}, "WIND_MED_PHY_HR_L3_MY_012_109": {"collection": "WIND_MED_PHY_HR_L3_MY_012_109"}, "WIND_MED_PHY_HR_L3_NRT_012_104": {"collection": "WIND_MED_PHY_HR_L3_NRT_012_104"}}}, "earth_search": {"product_types_config": {"cop-dem-glo-30": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme.", "instrument": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-30"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-90 provides worldwide coverage at 90 meters.", "instrument": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-90"}, "landsat-c2-l2": {"abstract": "Atmospherically corrected global Landsat Collection 2 Level-2 data from the Thematic Mapper (TM) onboard Landsat 4 and 5, the Enhanced Thematic Mapper Plus (ETM+) onboard Landsat 7, and the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard Landsat 8 and 9.", "instrument": "tm,etm+,oli,tirs", "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "missionStartDate": "1982-08-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "title": "Landsat Collection 2 Level-2"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) provides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). NAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) within the [US Department of Agriculture](https://www.usda.gov/) (USDA). Data are captured at least once every three years for each state. This dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "missionStartDate": "2010-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NAIP: National Agriculture Imagery Program"}, "sentinel-1-grd": {"abstract": "Sentinel-1 is a pair of Synthetic Aperture Radar (SAR) imaging satellites launched in 2014 and 2016 by the European Space Agency (ESA). Their 6 day revisit cycle and ability to observe through clouds makes this dataset perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to cloud-optimized GeoTIFF format.", "instrument": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "missionStartDate": "2014-10-10T00:28:21Z", "platform": "sentinel-1", "platformSerialIdentifier": "sentinel-1a,sentinel-1b", "processingLevel": null, "title": "Sentinel-1 Level-1C Ground Range Detected (GRD)"}, "sentinel-2-c1-l2a": {"abstract": "Sentinel-2 Collection 1 Level-2A, data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-c1-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Collection 1 Level-2A"}, "sentinel-2-l1c": {"abstract": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-l1c,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Level-1C"}, "sentinel-2-l2a": {"abstract": "Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Level-2A"}, "sentinel-2-pre-c1-l2a": {"abstract": "Sentinel-2 Pre-Collection 1 Level-2A (baseline < 05.00), with data and metadata matching collection sentinel-2-c1-l2a", "instrument": "msi", "keywords": "earth-observation,esa,msi,sentinel,sentinel-2,sentinel-2-pre-c1-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31.456000Z", "platform": "sentinel-2", "platformSerialIdentifier": "sentinel-2a,sentinel-2b", "processingLevel": null, "title": "Sentinel-2 Pre-Collection 1 Level-2A "}}, "providers_config": {"cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "naip": {"productType": "naip"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "sentinel-2-c1-l2a": {"productType": "sentinel-2-c1-l2a"}, "sentinel-2-l1c": {"productType": "sentinel-2-l1c"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "sentinel-2-pre-c1-l2a": {"productType": "sentinel-2-pre-c1-l2a"}}}, "eumetsat_ds": {"product_types_config": {"EO:EUM:CM:METOP:ASCSZFR02": {"abstract": null, "instrument": null, "keywords": "eo:eum:cm:metop:ascszfr02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:CM:METOP:ASCSZFR02"}, "EO:EUM:CM:METOP:ASCSZOR02": {"abstract": null, "instrument": null, "keywords": "eo:eum:cm:metop:ascszor02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:CM:METOP:ASCSZOR02"}, "EO:EUM:CM:METOP:ASCSZRR02": {"abstract": null, "instrument": null, "keywords": "eo:eum:cm:metop:ascszrr02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:CM:METOP:ASCSZRR02"}, "EO:EUM:DAT:0088": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0088", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0088"}, "EO:EUM:DAT:0142": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0142", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0142"}, "EO:EUM:DAT:0143": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0143", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0143"}, "EO:EUM:DAT:0236": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0236", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0236"}, "EO:EUM:DAT:0237": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0237", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0237"}, "EO:EUM:DAT:0238": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0238", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0238"}, "EO:EUM:DAT:0239": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0239", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0239"}, "EO:EUM:DAT:0240": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0240", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0240"}, "EO:EUM:DAT:0241": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0241", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0241"}, "EO:EUM:DAT:0274": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0274", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0274"}, "EO:EUM:DAT:0300": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0300", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0300"}, "EO:EUM:DAT:0301": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0301", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0301"}, "EO:EUM:DAT:0302": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0302", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0302"}, "EO:EUM:DAT:0303": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0303", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0303"}, "EO:EUM:DAT:0305": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0305", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0305"}, "EO:EUM:DAT:0343": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0343", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0343"}, "EO:EUM:DAT:0344": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0344", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0344"}, "EO:EUM:DAT:0345": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0345", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0345"}, "EO:EUM:DAT:0348": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0348", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0348"}, "EO:EUM:DAT:0349": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0349", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0349"}, "EO:EUM:DAT:0374": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0374", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0374"}, "EO:EUM:DAT:0394": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0394", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0394"}, "EO:EUM:DAT:0398": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0398", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0398"}, "EO:EUM:DAT:0405": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0405", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0405"}, "EO:EUM:DAT:0406": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0406", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0406"}, "EO:EUM:DAT:0407": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0407", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0407"}, "EO:EUM:DAT:0408": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0408", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0408"}, "EO:EUM:DAT:0409": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0409", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0409"}, "EO:EUM:DAT:0410": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0410", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0410"}, "EO:EUM:DAT:0411": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0411", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0411"}, "EO:EUM:DAT:0412": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0412", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0412"}, "EO:EUM:DAT:0413": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0413", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0413"}, "EO:EUM:DAT:0414": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0414", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0414"}, "EO:EUM:DAT:0415": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0415", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0415"}, "EO:EUM:DAT:0416": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0416", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0416"}, "EO:EUM:DAT:0417": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0417", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0417"}, "EO:EUM:DAT:0533": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0533", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0533"}, "EO:EUM:DAT:0556": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0556", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0556"}, "EO:EUM:DAT:0557": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0557", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0557"}, "EO:EUM:DAT:0558": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0558", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0558"}, "EO:EUM:DAT:0576": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0576", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0576"}, "EO:EUM:DAT:0577": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0577", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0577"}, "EO:EUM:DAT:0578": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0578", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0578"}, "EO:EUM:DAT:0579": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0579", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0579"}, "EO:EUM:DAT:0581": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0581", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0581"}, "EO:EUM:DAT:0582": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0582", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0582"}, "EO:EUM:DAT:0583": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0583", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0583"}, "EO:EUM:DAT:0584": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0584", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0584"}, "EO:EUM:DAT:0585": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0585", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0585"}, "EO:EUM:DAT:0586": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0586", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0586"}, "EO:EUM:DAT:0601": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0601", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0601"}, "EO:EUM:DAT:0615": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0615", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0615"}, "EO:EUM:DAT:0617": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0617", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0617"}, "EO:EUM:DAT:0645": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0645", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0645"}, "EO:EUM:DAT:0647": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0647", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0647"}, "EO:EUM:DAT:0662": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0662", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0662"}, "EO:EUM:DAT:0665": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0665", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0665"}, "EO:EUM:DAT:0686": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0686", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0686"}, "EO:EUM:DAT:0687": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0687", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0687"}, "EO:EUM:DAT:0688": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0688", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0688"}, "EO:EUM:DAT:0690": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0690", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0690"}, "EO:EUM:DAT:0691": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0691", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0691"}, "EO:EUM:DAT:0758": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0758", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0758"}, "EO:EUM:DAT:0782": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0782", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0782"}, "EO:EUM:DAT:0833": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0833", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0833"}, "EO:EUM:DAT:0834": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0834", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0834"}, "EO:EUM:DAT:0835": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0835", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0835"}, "EO:EUM:DAT:0836": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0836", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0836"}, "EO:EUM:DAT:0837": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0837", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0837"}, "EO:EUM:DAT:0838": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0838", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0838"}, "EO:EUM:DAT:0839": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0839", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0839"}, "EO:EUM:DAT:0840": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0840", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0840"}, "EO:EUM:DAT:0841": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0841", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0841"}, "EO:EUM:DAT:0842": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0842", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0842"}, "EO:EUM:DAT:0850": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0850", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0850"}, "EO:EUM:DAT:0851": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0851", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0851"}, "EO:EUM:DAT:0852": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0852", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0852"}, "EO:EUM:DAT:0853": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0853", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0853"}, "EO:EUM:DAT:0854": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0854", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0854"}, "EO:EUM:DAT:0855": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0855", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0855"}, "EO:EUM:DAT:0856": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0856", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0856"}, "EO:EUM:DAT:0857": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0857", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0857"}, "EO:EUM:DAT:0858": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0858", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0858"}, "EO:EUM:DAT:0859": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0859", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0859"}, "EO:EUM:DAT:0862": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0862", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0862"}, "EO:EUM:DAT:0863": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0863", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0863"}, "EO:EUM:DAT:0880": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0880", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0880"}, "EO:EUM:DAT:0881": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0881", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0881"}, "EO:EUM:DAT:0882": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0882", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0882"}, "EO:EUM:DAT:0894": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0894", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0894"}, "EO:EUM:DAT:0895": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0895", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0895"}, "EO:EUM:DAT:0959": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0959", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0959"}, "EO:EUM:DAT:0960": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0960", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0960"}, "EO:EUM:DAT:0961": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0961", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0961"}, "EO:EUM:DAT:0962": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0962", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0962"}, "EO:EUM:DAT:0963": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0963", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0963"}, "EO:EUM:DAT:0964": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0964", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0964"}, "EO:EUM:DAT:0986": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0986", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0986"}, "EO:EUM:DAT:0987": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:0987", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:0987"}, "EO:EUM:DAT:DMSP:OSI-401-B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:dmsp:osi-401-b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:DMSP:OSI-401-B"}, "EO:EUM:DAT:METOP:AMSUL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:amsul1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:AMSUL1"}, "EO:EUM:DAT:METOP:ASCSZF1B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:ascszf1b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:ASCSZF1B"}, "EO:EUM:DAT:METOP:ASCSZO1B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:ascszo1b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:ASCSZO1B"}, "EO:EUM:DAT:METOP:ASCSZR1B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:ascszr1b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:ASCSZR1B"}, "EO:EUM:DAT:METOP:AVHRRL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:avhrrl1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:AVHRRL1"}, "EO:EUM:DAT:METOP:GLB-SST-NC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:glb-sst-nc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:GLB-SST-NC"}, "EO:EUM:DAT:METOP:GOMEL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:gomel1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:GOMEL1"}, "EO:EUM:DAT:METOP:IASIL1C-ALL": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:iasil1c-all", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:IASIL1C-ALL"}, "EO:EUM:DAT:METOP:IASIL2COX": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:iasil2cox", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:IASIL2COX"}, "EO:EUM:DAT:METOP:IASSND02": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:iassnd02", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:IASSND02"}, "EO:EUM:DAT:METOP:LSA-002": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:lsa-002", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:LSA-002"}, "EO:EUM:DAT:METOP:MHSL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:mhsl1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:MHSL1"}, "EO:EUM:DAT:METOP:NTO": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:nto", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:NTO"}, "EO:EUM:DAT:METOP:OAS025": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:oas025", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OAS025"}, "EO:EUM:DAT:METOP:OSI-104": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:osi-104", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OSI-104"}, "EO:EUM:DAT:METOP:OSI-150-A": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:osi-150-a", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OSI-150-A"}, "EO:EUM:DAT:METOP:OSI-150-B": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:osi-150-b", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:OSI-150-B"}, "EO:EUM:DAT:METOP:SOMO12": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:somo12", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:SOMO12"}, "EO:EUM:DAT:METOP:SOMO25": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:metop:somo25", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:METOP:SOMO25"}, "EO:EUM:DAT:MSG:CLM": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:clm", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:CLM"}, "EO:EUM:DAT:MSG:CLM-IODC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:clm-iodc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:CLM-IODC"}, "EO:EUM:DAT:MSG:HRSEVIRI": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:hrseviri", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:HRSEVIRI"}, "EO:EUM:DAT:MSG:HRSEVIRI-IODC": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:hrseviri-iodc", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:HRSEVIRI-IODC"}, "EO:EUM:DAT:MSG:MSG15-RSS": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:msg15-rss", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:MSG15-RSS"}, "EO:EUM:DAT:MSG:RSS-CLM": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:msg:rss-clm", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MSG:RSS-CLM"}, "EO:EUM:DAT:MULT:HIRSL1": {"abstract": null, "instrument": null, "keywords": "eo:eum:dat:mult:hirsl1", "license": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EO:EUM:DAT:MULT:HIRSL1"}}, "providers_config": {"EO:EUM:CM:METOP:ASCSZFR02": {"parentIdentifier": "EO:EUM:CM:METOP:ASCSZFR02"}, "EO:EUM:CM:METOP:ASCSZOR02": {"parentIdentifier": "EO:EUM:CM:METOP:ASCSZOR02"}, "EO:EUM:CM:METOP:ASCSZRR02": {"parentIdentifier": "EO:EUM:CM:METOP:ASCSZRR02"}, "EO:EUM:DAT:0088": {"parentIdentifier": "EO:EUM:DAT:0088"}, "EO:EUM:DAT:0142": {"parentIdentifier": "EO:EUM:DAT:0142"}, "EO:EUM:DAT:0143": {"parentIdentifier": "EO:EUM:DAT:0143"}, "EO:EUM:DAT:0236": {"parentIdentifier": "EO:EUM:DAT:0236"}, "EO:EUM:DAT:0237": {"parentIdentifier": "EO:EUM:DAT:0237"}, "EO:EUM:DAT:0238": {"parentIdentifier": "EO:EUM:DAT:0238"}, "EO:EUM:DAT:0239": {"parentIdentifier": "EO:EUM:DAT:0239"}, "EO:EUM:DAT:0240": {"parentIdentifier": "EO:EUM:DAT:0240"}, "EO:EUM:DAT:0241": {"parentIdentifier": "EO:EUM:DAT:0241"}, "EO:EUM:DAT:0274": {"parentIdentifier": "EO:EUM:DAT:0274"}, "EO:EUM:DAT:0300": {"parentIdentifier": "EO:EUM:DAT:0300"}, "EO:EUM:DAT:0301": {"parentIdentifier": "EO:EUM:DAT:0301"}, "EO:EUM:DAT:0302": {"parentIdentifier": "EO:EUM:DAT:0302"}, "EO:EUM:DAT:0303": {"parentIdentifier": "EO:EUM:DAT:0303"}, "EO:EUM:DAT:0305": {"parentIdentifier": "EO:EUM:DAT:0305"}, "EO:EUM:DAT:0343": {"parentIdentifier": "EO:EUM:DAT:0343"}, "EO:EUM:DAT:0344": {"parentIdentifier": "EO:EUM:DAT:0344"}, "EO:EUM:DAT:0345": {"parentIdentifier": "EO:EUM:DAT:0345"}, "EO:EUM:DAT:0348": {"parentIdentifier": "EO:EUM:DAT:0348"}, "EO:EUM:DAT:0349": {"parentIdentifier": "EO:EUM:DAT:0349"}, "EO:EUM:DAT:0374": {"parentIdentifier": "EO:EUM:DAT:0374"}, "EO:EUM:DAT:0394": {"parentIdentifier": "EO:EUM:DAT:0394"}, "EO:EUM:DAT:0398": {"parentIdentifier": "EO:EUM:DAT:0398"}, "EO:EUM:DAT:0405": {"parentIdentifier": "EO:EUM:DAT:0405"}, "EO:EUM:DAT:0406": {"parentIdentifier": "EO:EUM:DAT:0406"}, "EO:EUM:DAT:0407": {"parentIdentifier": "EO:EUM:DAT:0407"}, "EO:EUM:DAT:0408": {"parentIdentifier": "EO:EUM:DAT:0408"}, "EO:EUM:DAT:0409": {"parentIdentifier": "EO:EUM:DAT:0409"}, "EO:EUM:DAT:0410": {"parentIdentifier": "EO:EUM:DAT:0410"}, "EO:EUM:DAT:0411": {"parentIdentifier": "EO:EUM:DAT:0411"}, "EO:EUM:DAT:0412": {"parentIdentifier": "EO:EUM:DAT:0412"}, "EO:EUM:DAT:0413": {"parentIdentifier": "EO:EUM:DAT:0413"}, "EO:EUM:DAT:0414": {"parentIdentifier": "EO:EUM:DAT:0414"}, "EO:EUM:DAT:0415": {"parentIdentifier": "EO:EUM:DAT:0415"}, "EO:EUM:DAT:0416": {"parentIdentifier": "EO:EUM:DAT:0416"}, "EO:EUM:DAT:0417": {"parentIdentifier": "EO:EUM:DAT:0417"}, "EO:EUM:DAT:0533": {"parentIdentifier": "EO:EUM:DAT:0533"}, "EO:EUM:DAT:0556": {"parentIdentifier": "EO:EUM:DAT:0556"}, "EO:EUM:DAT:0557": {"parentIdentifier": "EO:EUM:DAT:0557"}, "EO:EUM:DAT:0558": {"parentIdentifier": "EO:EUM:DAT:0558"}, "EO:EUM:DAT:0576": {"parentIdentifier": "EO:EUM:DAT:0576"}, "EO:EUM:DAT:0577": {"parentIdentifier": "EO:EUM:DAT:0577"}, "EO:EUM:DAT:0578": {"parentIdentifier": "EO:EUM:DAT:0578"}, "EO:EUM:DAT:0579": {"parentIdentifier": "EO:EUM:DAT:0579"}, "EO:EUM:DAT:0581": {"parentIdentifier": "EO:EUM:DAT:0581"}, "EO:EUM:DAT:0582": {"parentIdentifier": "EO:EUM:DAT:0582"}, "EO:EUM:DAT:0583": {"parentIdentifier": "EO:EUM:DAT:0583"}, "EO:EUM:DAT:0584": {"parentIdentifier": "EO:EUM:DAT:0584"}, "EO:EUM:DAT:0585": {"parentIdentifier": "EO:EUM:DAT:0585"}, "EO:EUM:DAT:0586": {"parentIdentifier": "EO:EUM:DAT:0586"}, "EO:EUM:DAT:0601": {"parentIdentifier": "EO:EUM:DAT:0601"}, "EO:EUM:DAT:0615": {"parentIdentifier": "EO:EUM:DAT:0615"}, "EO:EUM:DAT:0617": {"parentIdentifier": "EO:EUM:DAT:0617"}, "EO:EUM:DAT:0645": {"parentIdentifier": "EO:EUM:DAT:0645"}, "EO:EUM:DAT:0647": {"parentIdentifier": "EO:EUM:DAT:0647"}, "EO:EUM:DAT:0662": {"parentIdentifier": "EO:EUM:DAT:0662"}, "EO:EUM:DAT:0665": {"parentIdentifier": "EO:EUM:DAT:0665"}, "EO:EUM:DAT:0686": {"parentIdentifier": "EO:EUM:DAT:0686"}, "EO:EUM:DAT:0687": {"parentIdentifier": "EO:EUM:DAT:0687"}, "EO:EUM:DAT:0688": {"parentIdentifier": "EO:EUM:DAT:0688"}, "EO:EUM:DAT:0690": {"parentIdentifier": "EO:EUM:DAT:0690"}, "EO:EUM:DAT:0691": {"parentIdentifier": "EO:EUM:DAT:0691"}, "EO:EUM:DAT:0758": {"parentIdentifier": "EO:EUM:DAT:0758"}, "EO:EUM:DAT:0782": {"parentIdentifier": "EO:EUM:DAT:0782"}, "EO:EUM:DAT:0833": {"parentIdentifier": "EO:EUM:DAT:0833"}, "EO:EUM:DAT:0834": {"parentIdentifier": "EO:EUM:DAT:0834"}, "EO:EUM:DAT:0835": {"parentIdentifier": "EO:EUM:DAT:0835"}, "EO:EUM:DAT:0836": {"parentIdentifier": "EO:EUM:DAT:0836"}, "EO:EUM:DAT:0837": {"parentIdentifier": "EO:EUM:DAT:0837"}, "EO:EUM:DAT:0838": {"parentIdentifier": "EO:EUM:DAT:0838"}, "EO:EUM:DAT:0839": {"parentIdentifier": "EO:EUM:DAT:0839"}, "EO:EUM:DAT:0840": {"parentIdentifier": "EO:EUM:DAT:0840"}, "EO:EUM:DAT:0841": {"parentIdentifier": "EO:EUM:DAT:0841"}, "EO:EUM:DAT:0842": {"parentIdentifier": "EO:EUM:DAT:0842"}, "EO:EUM:DAT:0850": {"parentIdentifier": "EO:EUM:DAT:0850"}, "EO:EUM:DAT:0851": {"parentIdentifier": "EO:EUM:DAT:0851"}, "EO:EUM:DAT:0852": {"parentIdentifier": "EO:EUM:DAT:0852"}, "EO:EUM:DAT:0853": {"parentIdentifier": "EO:EUM:DAT:0853"}, "EO:EUM:DAT:0854": {"parentIdentifier": "EO:EUM:DAT:0854"}, "EO:EUM:DAT:0855": {"parentIdentifier": "EO:EUM:DAT:0855"}, "EO:EUM:DAT:0856": {"parentIdentifier": "EO:EUM:DAT:0856"}, "EO:EUM:DAT:0857": {"parentIdentifier": "EO:EUM:DAT:0857"}, "EO:EUM:DAT:0858": {"parentIdentifier": "EO:EUM:DAT:0858"}, "EO:EUM:DAT:0859": {"parentIdentifier": "EO:EUM:DAT:0859"}, "EO:EUM:DAT:0862": {"parentIdentifier": "EO:EUM:DAT:0862"}, "EO:EUM:DAT:0863": {"parentIdentifier": "EO:EUM:DAT:0863"}, "EO:EUM:DAT:0880": {"parentIdentifier": "EO:EUM:DAT:0880"}, "EO:EUM:DAT:0881": {"parentIdentifier": "EO:EUM:DAT:0881"}, "EO:EUM:DAT:0882": {"parentIdentifier": "EO:EUM:DAT:0882"}, "EO:EUM:DAT:0894": {"parentIdentifier": "EO:EUM:DAT:0894"}, "EO:EUM:DAT:0895": {"parentIdentifier": "EO:EUM:DAT:0895"}, "EO:EUM:DAT:0959": {"parentIdentifier": "EO:EUM:DAT:0959"}, "EO:EUM:DAT:0960": {"parentIdentifier": "EO:EUM:DAT:0960"}, "EO:EUM:DAT:0961": {"parentIdentifier": "EO:EUM:DAT:0961"}, "EO:EUM:DAT:0962": {"parentIdentifier": "EO:EUM:DAT:0962"}, "EO:EUM:DAT:0963": {"parentIdentifier": "EO:EUM:DAT:0963"}, "EO:EUM:DAT:0964": {"parentIdentifier": "EO:EUM:DAT:0964"}, "EO:EUM:DAT:0986": {"parentIdentifier": "EO:EUM:DAT:0986"}, "EO:EUM:DAT:0987": {"parentIdentifier": "EO:EUM:DAT:0987"}, "EO:EUM:DAT:DMSP:OSI-401-B": {"parentIdentifier": "EO:EUM:DAT:DMSP:OSI-401-B"}, "EO:EUM:DAT:METOP:AMSUL1": {"parentIdentifier": "EO:EUM:DAT:METOP:AMSUL1"}, "EO:EUM:DAT:METOP:ASCSZF1B": {"parentIdentifier": "EO:EUM:DAT:METOP:ASCSZF1B"}, "EO:EUM:DAT:METOP:ASCSZO1B": {"parentIdentifier": "EO:EUM:DAT:METOP:ASCSZO1B"}, "EO:EUM:DAT:METOP:ASCSZR1B": {"parentIdentifier": "EO:EUM:DAT:METOP:ASCSZR1B"}, "EO:EUM:DAT:METOP:AVHRRL1": {"parentIdentifier": "EO:EUM:DAT:METOP:AVHRRL1"}, "EO:EUM:DAT:METOP:GLB-SST-NC": {"parentIdentifier": "EO:EUM:DAT:METOP:GLB-SST-NC"}, "EO:EUM:DAT:METOP:GOMEL1": {"parentIdentifier": "EO:EUM:DAT:METOP:GOMEL1"}, "EO:EUM:DAT:METOP:IASIL1C-ALL": {"parentIdentifier": "EO:EUM:DAT:METOP:IASIL1C-ALL"}, "EO:EUM:DAT:METOP:IASIL2COX": {"parentIdentifier": "EO:EUM:DAT:METOP:IASIL2COX"}, "EO:EUM:DAT:METOP:IASSND02": {"parentIdentifier": "EO:EUM:DAT:METOP:IASSND02"}, "EO:EUM:DAT:METOP:LSA-002": {"parentIdentifier": "EO:EUM:DAT:METOP:LSA-002"}, "EO:EUM:DAT:METOP:MHSL1": {"parentIdentifier": "EO:EUM:DAT:METOP:MHSL1"}, "EO:EUM:DAT:METOP:NTO": {"parentIdentifier": "EO:EUM:DAT:METOP:NTO"}, "EO:EUM:DAT:METOP:OAS025": {"parentIdentifier": "EO:EUM:DAT:METOP:OAS025"}, "EO:EUM:DAT:METOP:OSI-104": {"parentIdentifier": "EO:EUM:DAT:METOP:OSI-104"}, "EO:EUM:DAT:METOP:OSI-150-A": {"parentIdentifier": "EO:EUM:DAT:METOP:OSI-150-A"}, "EO:EUM:DAT:METOP:OSI-150-B": {"parentIdentifier": "EO:EUM:DAT:METOP:OSI-150-B"}, "EO:EUM:DAT:METOP:SOMO12": {"parentIdentifier": "EO:EUM:DAT:METOP:SOMO12"}, "EO:EUM:DAT:METOP:SOMO25": {"parentIdentifier": "EO:EUM:DAT:METOP:SOMO25"}, "EO:EUM:DAT:MSG:CLM": {"parentIdentifier": "EO:EUM:DAT:MSG:CLM"}, "EO:EUM:DAT:MSG:CLM-IODC": {"parentIdentifier": "EO:EUM:DAT:MSG:CLM-IODC"}, "EO:EUM:DAT:MSG:HRSEVIRI": {"parentIdentifier": "EO:EUM:DAT:MSG:HRSEVIRI"}, "EO:EUM:DAT:MSG:HRSEVIRI-IODC": {"parentIdentifier": "EO:EUM:DAT:MSG:HRSEVIRI-IODC"}, "EO:EUM:DAT:MSG:MSG15-RSS": {"parentIdentifier": "EO:EUM:DAT:MSG:MSG15-RSS"}, "EO:EUM:DAT:MSG:RSS-CLM": {"parentIdentifier": "EO:EUM:DAT:MSG:RSS-CLM"}, "EO:EUM:DAT:MULT:HIRSL1": {"parentIdentifier": "EO:EUM:DAT:MULT:HIRSL1"}}}, "geodes": {"product_types_config": {"FLATSIM_AFAR_AUXILIARYDATA_PUBLIC": {"abstract": "This project aims to characterize the spatial and temporal distribution of the deformation in the region of the Afar depression. The aim is to better understand large-scale tectonics (localization of divergent borders, kinematics of the triple point), but also the dynamics of volcanic, seismic and aseismic events, and the mechanics of active faults. The issue of gravity hazard will also be addressed. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, etc.", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-afar-auxiliarydata-public,ground-to-radar,insar,landslides,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping,volcanology", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Afar interferograms"}, "FLATSIM_AFAR_INTERFEROGRAM_PUBLIC": {"abstract": "This project aims to characterize the spatial and temporal distribution of the deformation in the region of the Afar depression. The aim is to better understand large-scale tectonics (localization of divergent borders, kinematics of the triple point), but also the dynamics of volcanic, seismic and aseismic events, and the mechanics of active faults. The issue of gravity hazard will also be addressed. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-afar-interferogram-public,ground-geometry,insar,interferogram,landslides,radar-geometry,tectonics,unwrapped,volcanology,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Afar interferograms"}, "FLATSIM_AFAR_TIMESERIE_PUBLIC": {"abstract": "This project aims to characterize the spatial and temporal distribution of the deformation in the region of the Afar depression. The aim is to better understand large-scale tectonics (localization of divergent borders, kinematics of the triple point), but also the dynamics of volcanic, seismic and aseismic events, and the mechanics of active faults. The issue of gravity hazard will also be addressed. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-afar-timeserie-public,ground-geometry,insar,landslides,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie,volcanology", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Afar time series"}, "FLATSIM_ANDES_AUXILIARYDATA_PUBLIC": {"abstract": "This project aims to better understand the seismic cycle of the Andean subduction zone in Peru and Chile and the crustal faults in the Andes, to follow the functioning of the large active volcanoes in the region (and to detect possible precursors to eruptions) , and to study the seismic, climatic and anthropogenic forcing on the dynamics of landslides in the Andes. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, etc.", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-andes-auxiliarydata-public,ground-to-radar,insar,landslides,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping,volcanology", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Andes auxiliary data"}, "FLATSIM_ANDES_INTERFEROGRAM_PUBLIC": {"abstract": "This project aims to better understand the seismic cycle of the Andean subduction zone in Peru and Chile and the crustal faults in the Andes, to follow the functioning of the large active volcanoes in the region (and to detect possible precursors to eruptions) , and to study the seismic, climatic and anthropogenic forcing on the dynamics of landslides in the Andes. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-andes-interferogram-public,ground-geometry,insar,interferogram,landslides,radar-geometry,tectonics,unwrapped,volcanology,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Andes interferograms"}, "FLATSIM_ANDES_TIMESERIE_PUBLIC": {"abstract": "This project aims to better understand the seismic cycle of the Andean subduction zone in Peru and Chile and the crustal faults in the Andes, to follow the functioning of the large active volcanoes in the region (and to detect possible precursors to eruptions) , and to study the seismic, climatic and anthropogenic forcing on the dynamics of landslides in the Andes. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-andes-timeserie-public,ground-geometry,insar,landslides,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie,volcanology", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Andes time series"}, "FLATSIM_BALKANS_AUXILIARYDATA_PUBLIC": {"abstract": "The Balkan region is one of the most seismic zones in Europe, with intense industrial and demographic development. This project aims to better quantify the deformations of tectonic origin in this region (kinematics and localization of active faults, study of earthquakes). He is also interested in deformations of anthropogenic or climatic origin (linked to the exploitation of natural resources or to variations in sea level). This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026.", "instrument": null, "keywords": "amplitude,anthropogenic-and-climatic-hazards,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-balkans-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Balkans auxiliary data"}, "FLATSIM_BALKANS_INTERFEROGRAM_PUBLIC": {"abstract": "The Balkan region is one of the most seismic zones in Europe, with intense industrial and demographic development. This project aims to better quantify the deformations of tectonic origin in this region (kinematics and localization of active faults, study of earthquakes). He is also interested in deformations of anthropogenic or climatic origin (linked to the exploitation of natural resources or to variations in sea level). Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "anthropogenic-and-climatic-hazards,atmospheric-phase-screen,coherence,deformation,flatsim-balkans-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Balkans interferograms"}, "FLATSIM_BALKANS_TIMESERIE_PUBLIC": {"abstract": "The Balkan region is one of the most seismic zones in Europe, with intense industrial and demographic development. This project aims to better quantify the deformations of tectonic origin in this region (kinematics and localization of active faults, study of earthquakes). He is also interested in deformations of anthropogenic or climatic origin (linked to the exploitation of natural resources or to variations in sea level). This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "anthropogenic-and-climatic-hazards,data-cube,deformation,flatsim-balkans-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Balkans time series"}, "FLATSIM_CAUCASE_AUXILIARYDATA_PUBLIC": {"abstract": "The aim of the project is to gain a better understanding of the distribution of deformation associated with convergence between Arabia and Eurasia in the Caucasus region, where various reverse and strike-slip faults with high seismic hazard co-exist. It should enable to propose a regional kinematic and seismo-tectonic model, and quantify vertical movements in particular. Complementary objectives include monitoring mud volcanoes in Azerbaijan, which erupt frequently, and anthropogenic deformation.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-caucase-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Caucasus auxiliary data"}, "FLATSIM_CAUCASE_INTERFEROGRAM_PUBLIC": {"abstract": "The aim of the project is to gain a better understanding of the distribution of deformation associated with convergence between Arabia and Eurasia in the Caucasus region, where various reverse and strike-slip faults with high seismic hazard co-exist. It should enable to propose a regional kinematic and seismo-tectonic model, and quantify vertical movements in particular. Complementary objectives include monitoring mud volcanoes in Azerbaijan, which erupt frequently, and anthropogenic deformation. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-caucase-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Caucasus interferograms"}, "FLATSIM_CAUCASE_TIMESERIE_PUBLIC": {"abstract": "The aim of the project is to gain a better understanding of the distribution of deformation associated with convergence between Arabia and Eurasia in the Caucasus region, where various reverse and strike-slip faults with high seismic hazard co-exist. It should enable to propose a regional kinematic and seismo-tectonic model, and quantify vertical movements in particular. Complementary objectives include monitoring mud volcanoes in Azerbaijan, which erupt frequently, and anthropogenic deformation.This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-caucase-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Caucasus time series"}, "FLATSIM_CHILI_AUXILIARYDATA_PUBLIC": {"abstract": "The project aims to quantify the deformations associated with the Andean subduction in central Chile, with the main objectives to (1) constrain the kinematics and interseismic coupling of the subduction interface and crustal faults, at the front and inside the Andes mountain range, (2) analyze co- and post-seismic deformations during different seismic crises and slow slip episodes, (3) understand the link between the seismic cycle and relief building, (4) monitor the behavior of volcanoes in the Andean arc. Another objective is to characterize erosion episodes during extreme climatic events in the Atacama Desert. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-chili-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Chile auxiliary data"}, "FLATSIM_CHILI_INTERFEROGRAM_PUBLIC": {"abstract": "The project aims to quantify the deformations associated with the Andean subduction in central Chile, with the main objectives to (1) constrain the kinematics and interseismic coupling of the subduction interface and crustal faults, at the front and inside the Andes mountain range,(2) analyze co- and post-seismic deformations during different seismic crises and slow slip episodes,(3) understand the link between the seismic cycle and relief building,(4) monitor the behavior of volcanoes in the Andean arc. Another objective is to characterize erosion episodes during extreme climatic events in the Atacama Desert.Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-chili-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Chile interferograms"}, "FLATSIM_CHILI_TIMESERIE_PUBLIC": {"abstract": "The project aims to quantify the deformations associated with the Andean subduction in central Chile, with the main objectives to (1) constrain the kinematics and interseismic coupling of the subduction interface and crustal faults, at the front and inside the Andes mountain range, (2) analyze co- and post-seismic deformations during different seismic crises and slow slip episodes, (3) understand the link between the seismic cycle and relief building, (4) monitor the behavior of volcanoes in the Andean arc. Another objective is to characterize erosion episodes during extreme climatic events in the Atacama Desert.This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-chili-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Central Chile time series"}, "FLATSIM_CORSE_AUXILIARYDATA_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog.The project focuses on tectonic, hydrological, landslides, subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.)This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-corse-auxiliarydata-public,ground-to-radar,hydrology,insar,landslides,lookup-tables,radar-to-ground,spectral-diversity,subsidences,tectonic,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Corse auxiliary data"}, "FLATSIM_CORSE_INTERFEROGRAM_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog.The project focuses on tectonic, hydrological, landslides, subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.) Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-corse-interferogram-public,ground-geometry,hydrology,insar,interferogram,landslides,radar-geometry,subsidences,tectonic,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Corse interferograms"}, "FLATSIM_CORSE_TIMESERIE_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog. The project focuses on tectonic, hydrological, landslides, subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.) This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-corse-timeserie-public,ground-geometry,hydrology,insar,landslides,mean-los-velocity,quality-maps,radar-geometry,stack-list,subsidences,tectonic,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Corse time series"}, "FLATSIM_FRANCE_AUXILIARYDATA_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog.The project focuses on tectonic, hydrological, landslides, rock glaciers and subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.).This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-france-auxiliarydata-public,ground-to-radar,hydrology,insar,landslides,lookup-tables,radar-to-ground,rock-glaciers,spectral-diversity,subsidence,tectonic,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM France auxiliary data"}, "FLATSIM_FRANCE_INTERFEROGRAM_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform. After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog. The project focuses on tectonic, hydrological, landslides, rock glaciers and subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.).Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-france-interferogram-public,ground-geometry,hydrology,insar,interferogram,landslides,radar-geometry,rock-glaciers,subsidence,tectonic,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM France interferograms"}, "FLATSIM_FRANCE_TIMESERIE_PUBLIC": {"abstract": "FLATSIM products have also been calculated for the whole of mainland France in the SNO ISDeform.After post-processing at ISTerre (UGA) and validation by ISDeform (during 2024), these products will also be made available via a dedicated collection in the FormaTerre catalog.The project focuses on tectonic, hydrological, landslides, rock glaciers and subsidence applications. It can also be used to monitor the compaction of sedimentary basins, deformations associated with the exploitation of the subsoil (storage, mining, hydrothermalism) and certain infrastructures (embankments, etc.).This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-france-timeserie-public,ground-geometry,hydrology,insar,landslides,mean-los-velocity,quality-maps,radar-geometry,rock-glaciers,stack-list,subsidence,tectonic,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM France time series"}, "FLATSIM_INDE_AUXILIARYDATA_PUBLIC": {"abstract": "Activation of the CIEST2 process to schedule the acquisition of Pleiades stereo images following a request from scientists for a gravitational collapse in the city of Joshimath, India. It has been proposed to supplement the acquisition of stereo images and optical processing with InSAR processing (FLATSIM service) in order to obtain information on the evolution of ground deformation. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-inde-auxiliarydata-public,ground-to-radar,insar,landslide-instability,lookup-tables,radar-to-ground,spectral-diversity,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Joshimath India auxiliary data"}, "FLATSIM_INDE_INTERFEROGRAM_PUBLIC": {"abstract": "Activation of the CIEST2 process to schedule the acquisition of Pleiades stereo images following a request from scientists for a gravitational collapse in the city of Joshimath, India. It has been proposed to supplement the acquisition of stereo images and optical processing with InSAR processing (FLATSIM service) in order to obtain information on the evolution of ground deformation. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-inde-interferogram-public,ground-geometry,insar,interferogram,landslide-instability,radar-geometry,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Joshimath India interferograms"}, "FLATSIM_INDE_TIMESERIE_PUBLIC": {"abstract": "Activation of the CIEST2 process to schedule the acquisition of Pleiades stereo images following a request from scientists for a gravitational collapse in the city of Joshimath, India. It has been proposed to supplement the acquisition of stereo images and optical processing with InSAR processing (FLATSIM service) in order to obtain information on the evolution of ground deformation. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-inde-timeserie-public,ground-geometry,insar,landslide-instability,mean-los-velocity,quality-maps,radar-geometry,stack-list,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Joshimath India time series"}, "FLATSIM_LEVANT_AUXILIARYDATA_PUBLIC": {"abstract": "The main objective of the project is to analyze the coupling distribution (i.e. segmentation into locked zones or aseismic slip zones) along the active fault system of the Levant, in relation to historical earthquake sequences and the physical properties of the faults, with a view to improving the estimation of seismic hazard. The project also includes the study of hydrological processes and their forcings (anthropogenic in particular, related to water resource management), or gravitational processes (landslides and factors controlling their onset and velocity).This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-levant-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Levant auxiliary data"}, "FLATSIM_LEVANT_INTERFEROGRAM_PUBLIC": {"abstract": "The main objective of the project is to analyze the coupling distribution (i.e. segmentation into locked zones or aseismic slip zones) along the active fault system of the Levant, in relation to historical earthquake sequences and the physical properties of the faults, with a view to improving the estimation of seismic hazard. The project also includes the study of hydrological processes and their forcings (anthropogenic in particular, related to water resource management), or gravitational processes (landslides and factors controlling their onset and velocity). Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-levant-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Levant interferograms"}, "FLATSIM_LEVANT_TIMESERIE_PUBLIC": {"abstract": "The main objective of the project is to analyze the coupling distribution (i.e. segmentation into locked zones or aseismic slip zones) along the active fault system of the Levant, in relation to historical earthquake sequences and the physical properties of the faults, with a view to improving the estimation of seismic hazard. The project also includes the study of hydrological processes and their forcings (anthropogenic in particular, related to water resource management), or gravitational processes (landslides and factors controlling their onset and velocity). This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-levant-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Levant time series"}, "FLATSIM_MAGHREB_AUXILIARYDATA_PUBLIC": {"abstract": "The main aim of this project is to quantify tectonic deformation across the Maghreb, in the African/Eurasian convergence zone. In particular, it aims to estimate the spatial distribution and temporal evolution of interseismic deformation or deformation associated with recent earthquakes on active faults, from the coast to the Saharan platform. Secondary themes (landslides, coastal subsidence, dune movement in the Sahara, anthropogenic deformation associated with major structures, natural resource extraction, agriculture, etc.) will also be addressed.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-maghreb-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Maghreb auxiliary data"}, "FLATSIM_MAGHREB_INTERFEROGRAM_PUBLIC": {"abstract": "The main aim of this project is to quantify tectonic deformation across the Maghreb, in the African/Eurasian convergence zone. In particular, it aims to estimate the spatial distribution and temporal evolution of interseismic deformation or deformation associated with recent earthquakes on active faults, from the coast to the Saharan platform. Secondary themes (landslides, coastal subsidence, dune movement in the Sahara, anthropogenic deformation associated with major structures, natural resource extraction, agriculture, etc.) will also be addressed.Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-maghreb-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Maghreb interferograms"}, "FLATSIM_MAGHREB_TIMESERIE_PUBLIC": {"abstract": "The main aim of this project is to quantify tectonic deformation across the Maghreb, in the African/Eurasian convergence zone. In particular, it aims to estimate the spatial distribution and temporal evolution of interseismic deformation or deformation associated with recent earthquakes on active faults, from the coast to the Saharan platform. Secondary themes (landslides, coastal subsidence, dune movement in the Sahara, anthropogenic deformation associated with major structures, natural resource extraction, agriculture, etc.) will also be addressed.This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-maghreb-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Maghreb time series"}, "FLATSIM_MAKRAN_AUXILIARYDATA_PUBLIC": {"abstract": "The project aims to analyze strain partitioning along the Makran active margin, a convergence zone between Arabia and Eurasia. This region is characterized by major thrust fault systems and laterally variable seismic behavior (large earthquakes in the east, more moderate seismicity in the west). The mode of strain accumulation on active tectonic structures will be analyzed together with the lithospheric structure and geology, in order to better constrain the seismic hazard and geodynamic history of the region.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-makran-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Makran auxiliary data"}, "FLATSIM_MAKRAN_INTERFEROGRAM_PUBLIC": {"abstract": "The project aims to analyze strain partitioning along the Makran active margin, a convergence zone between Arabia and Eurasia. This region is characterized by major thrust fault systems and laterally variable seismic behavior (large earthquakes in the east, more moderate seismicity in the west). The mode of strain accumulation on active tectonic structures will be analyzed together with the lithospheric structure and geology, in order to better constrain the seismic hazard and geodynamic history of the region.Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-makran-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Makran interferograms"}, "FLATSIM_MAKRAN_TIMESERIE_PUBLIC": {"abstract": "The project aims to analyze strain partitioning along the Makran active margin, a convergence zone between Arabia and Eurasia. This region is characterized by major thrust fault systems and laterally variable seismic behavior (large earthquakes in the east, more moderate seismicity in the west). The mode of strain accumulation on active tectonic structures will be analyzed together with the lithospheric structure and geology, in order to better constrain the seismic hazard and geodynamic history of the region.This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-makran-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Makran time series"}, "FLATSIM_MEXIQUE_AUXILIARYDATA_PUBLIC": {"abstract": "The first objective of this project is to constrain interseismic coupling along the Mexican subduction interface and its temporal variations; the aim is to better quantify the distribution between seismic and asismic slip (slow slip events) to better estimate the seismic potential of this subduction zone. A second objective is to study crustal deformations of the upper plate along the trans-Mexican volcanic belt: deformations of tectonic origin, related to crustal faults, and volcanic origin, or deformations of anthropic origin (subsidence of sedimentary basins in relation to overexploitation of aquifers). This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-mexique-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Southern Mexico auxiliary data"}, "FLATSIM_MEXIQUE_INTERFEROGRAM_PUBLIC": {"abstract": "The first objective of this project is to constrain interseismic coupling along the Mexican subduction interface and its temporal variations; the aim is to better quantify the distribution between seismic and asismic slip (slow slip events) to better estimate the seismic potential of this subduction zone. A second objective is to study crustal deformations of the upper plate along the trans-Mexican volcanic belt: deformations of tectonic origin, related to crustal faults, and volcanic origin, or deformations of anthropic origin (subsidence of sedimentary basins in relation to overexploitation of aquifers). Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-mexique-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Southern Mexico interferograms"}, "FLATSIM_MEXIQUE_TIMESERIE_PUBLIC": {"abstract": "The first objective of this project is to constrain interseismic coupling along the Mexican subduction interface and its temporal variations; the aim is to better quantify the distribution between seismic and asismic slip (slow slip events) to better estimate the seismic potential of this subduction zone. A second objective is to study crustal deformations of the upper plate along the trans-Mexican volcanic belt: deformations of tectonic origin, related to crustal faults, and volcanic origin, or deformations of anthropic origin (subsidence of sedimentary basins in relation to overexploitation of aquifers). This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-mexique-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Southern Mexico time series"}, "FLATSIM_MOZAMBIQUE_AUXILIARYDATA_PUBLIC": {"abstract": "This project focuses on the southern part of the East African Rift in the Mozambique region. This incipient plate boundary zone, with a low rate of extension, has experienced several Mw> 5 earthquakes since the 20th century. The aim of the project is to extract the tectonic signal from the InSAR time series in order to better characterize the active structures (normal faults) and their kinematics, which are still poorly constrained. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-mozambique-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Mozambique auxiliary data"}, "FLATSIM_MOZAMBIQUE_INTERFEROGRAM_PUBLIC": {"abstract": "This project focuses on the southern part of the East African Rift in the Mozambique region. This incipient plate boundary zone, with a low rate of extension, has experienced several Mw> 5 earthquakes since the 20th century. The aim of the project is to extract the tectonic signal from the InSAR time series in order to better characterize the active structures (normal faults) and their kinematics, which are still poorly constrained. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-mozambique-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Mozambique interferograms"}, "FLATSIM_MOZAMBIQUE_TIMESERIE_PUBLIC": {"abstract": "This project focuses on the southern part of the East African Rift in the Mozambique region. This incipient plate boundary zone, with a low rate of extension, has experienced several Mw> 5 earthquakes since the 20th century. The aim of the project is to extract the tectonic signal from the InSAR time series in order to better characterize the active structures (normal faults) and their kinematics, which are still poorly constrained. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-mozambique-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Mozambique time series"}, "FLATSIM_OKAVANGO_AUXILIARYDATA_PUBLIC": {"abstract": "This project aims to better understand the deformations of tectonic origin in the area of \u200b\u200bthe Okavango Delta (associated with the functioning of the Okavango rift and the East African rift), or of hydrological origin (linked in particular to the flood cycle. ), and the possible interactions between tectonics and hydrology in this region. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-okavango-auxiliarydata-public,ground-to-radar,hydrology,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Okavango auxiliary data"}, "FLATSIM_OKAVANGO_INTERFEROGRAM_PUBLIC": {"abstract": "This project aims to better understand the deformations of tectonic origin in the area of \u200b\u200bthe Okavango Delta (associated with the functioning of the Okavango rift and the East African rift), or of hydrological origin (linked in particular to the flood cycle. ), and the possible interactions between tectonics and hydrology in this region. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-okavango-interferogram-public,ground-geometry,hydrology,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Okavango interferograms"}, "FLATSIM_OKAVANGO_TIMESERIE_PUBLIC": {"abstract": "This project aims to better understand the deformations of tectonic origin in the area of \u200b\u200bthe Okavango Delta (associated with the functioning of the Okavango rift and the East African rift), or of hydrological origin (linked in particular to the flood cycle. ), and the possible interactions between tectonics and hydrology in this region. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-okavango-timeserie-public,ground-geometry,hydrology,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Okavango time series"}, "FLATSIM_OZARK_AUXILIARYDATA_PUBLIC": {"abstract": "This project focuses on the study of the deformations associated with the Ozark aquifer (south of the Mississippi basin) subjected to strong variations in groundwater level, and in neighboring regions where significant seismicity is observed, at strong seasonal component (New Madrid), or linked to wastewater injections (Oklahoma). The objective is to better understand the geodesic signature of the hydrological cycle. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026.", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-ozark-auxiliarydata-public,ground-to-radar,hydrology,insar,lookup-tables,radar-to-ground,spectral-diversity,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Ozark auxiliary data"}, "FLATSIM_OZARK_INTERFEROGRAM_PUBLIC": {"abstract": "This project focuses on the study of the deformations associated with the Ozark aquifer (south of the Mississippi basin) subjected to strong variations in groundwater level, and in neighboring regions where significant seismicity is observed, at strong seasonal component (New Madrid), or linked to wastewater injections (Oklahoma). The objective is to better understand the geodesic signature of the hydrological cycle. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-ozark-interferogram-public,ground-geometry,hydrology,insar,interferogram,radar-geometry,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Ozark interferograms"}, "FLATSIM_OZARK_TIMESERIE_PUBLIC": {"abstract": "This project focuses on the study of the deformations associated with the Ozark aquifer (south of the Mississippi basin) subjected to strong variations in groundwater level, and in neighboring regions where significant seismicity is observed, at strong seasonal component (New Madrid), or linked to wastewater injections (Oklahoma). The objective is to better understand the geodesic signature of the hydrological cycle. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-ozark-timeserie-public,ground-geometry,hydrology,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Ozark time series"}, "FLATSIM_SHOWCASE_AUXILIARYDATA_PUBLIC": {"abstract": "Although products are generally available on a temporary limited-access basis, a showcase collection has already been set up with a view to enhancing and promoting the quality of the service. This collection gives access to a sample of products from the various projects and associated collections, processed in the framework of calls for ideas. It enables users to explore the potential of the data processed by the FLATSIM service.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-showcase-auxiliarydata-public,ground-to-radar,insar,junit-vector,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Showcase auxiliary data"}, "FLATSIM_SHOWCASE_INTERFEROGRAM_PUBLIC": {"abstract": "Although products are generally available on a temporary limited-access basis, a showcase collection has already been set up with a view to enhancing and promoting the quality of the service. This collection gives access to a sample of products from the various projects and associated collections, processed in the framework of calls for ideas. It enables users to explore the potential of the data processed by the FLATSIM service. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-showcase-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Showcase interferograms"}, "FLATSIM_SHOWCASE_TIMESERIE_PUBLIC": {"abstract": "Although products are generally available on a temporary limited-access basis, a showcase collection has already been set up with a view to enhancing and promoting the quality of the service. This collection gives access to a sample of products from the various projects and associated collections, processed in the framework of calls for ideas. It enables users to explore the potential of the data processed by the FLATSIM service. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-showcase-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Showcase time series"}, "FLATSIM_TARIM_AUXILIARYDATA_PUBLIC": {"abstract": "This project focuses on the analysis of tectonic deformations along the Western Kunlun Range, on the northwestern edge of the Tibetan Plateau. This region is marked by the interaction between large strike-slip and thrust faults, with in particular the existence of one of the largest thrust sheet in the world, whose interseismic loading and the capacity to produce \u201cmega-earthquakes\u201d will be investigated. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-tarim-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tarim auxiliary data"}, "FLATSIM_TARIM_INTERFEROGRAM_PUBLIC": {"abstract": "This project focuses on the analysis of tectonic deformations along the Western Kunlun Range, on the northwestern edge of the Tibetan Plateau. This region is marked by the interaction between large strike-slip and thrust faults, with in particular the existence of one of the largest thrust sheet in the world, whose interseismic loading and the capacity to produce \u201cmega-earthquakes\u201d will be investigated. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-tarim-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tarim interferograms"}, "FLATSIM_TARIM_TIMESERIE_PUBLIC": {"abstract": "This project focuses on the analysis of tectonic deformations along the Western Kunlun Range, on the northwestern edge of the Tibetan Plateau. This region is marked by the interaction between large strike-slip and thrust faults, with in particular the existence of one of the largest thrust sheet in the world, whose interseismic loading and the capacity to produce \u201cmega-earthquakes\u201d will be investigated. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-tarim-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tarim time series"}, "FLATSIM_TIANSHAN_AUXILIARYDATA_PUBLIC": {"abstract": "The aim of the project is to analyze deformation across the intracontinental Tianshan mountain range, linked to the India/Asia collision. In particular, the aim is (1) to quantify the partitioning of deformation between thrust faults and strike-slip faults,(2) to gain a better understanding of the behavior of folds and thrusts during the seismic cycle, in the foothills and at the heart of the range,and (3) to document regional-scale slope phenomena (landslides, permafrost freeze-thaw deformation, solifluction, etc.), in relation to seismicity and climate change.This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-tianshan-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tian Shan auxiliary data"}, "FLATSIM_TIANSHAN_INTERFEROGRAM_PUBLIC": {"abstract": "The aim of the project is to analyze deformation across the intracontinental Tianshan mountain range, linked to the India/Asia collision. In particular, the aim is (1) to quantify the partitioning of deformation between thrust faults and strike-slip faults, (2) to gain a better understanding of the behavior of folds and thrusts during the seismic cycle, in the foothills and at the heart of the range, and (3) to document regional-scale slope phenomena (landslides, permafrost freeze-thaw deformation, solifluction, etc.), in relation to seismicity and climate change. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-tianshan-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tian Shan interferograms"}, "FLATSIM_TIANSHAN_TIMESERIE_PUBLIC": {"abstract": "The aim of the project is to analyze deformation across the intracontinental Tianshan mountain range, linked to the India/Asia collision. In particular, the aim is (1) to quantify the partitioning of deformation between thrust faults and strike-slip faults, (2) to gain a better understanding of the behavior of folds and thrusts during the seismic cycle, in the foothills and at the heart of the range, and (3) to document regional-scale slope phenomena (landslides, permafrost freeze-thaw deformation, solifluction, etc.), in relation to seismicity and climate change. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time. ", "instrument": null, "keywords": "data-cube,deformation,flatsim-tianshan-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Tian Shan time series"}, "FLATSIM_TIBETHIM_AUXILIARYDATA_PUBLIC": {"abstract": "This project complements the East Tibet project of the 1st FLATSIM call, with the aim of quantifying the tectonic deformations associated with the India-Asia convergence, from the Himalayan front and across the Tibetan plateau, characterizing lithospheric rheology and hydrological, landslides and cryosphere-related processes. Possible links between the seismic cycle, external forcings and relief construction will be explored. More methodological aspects, such as the quasi-absolute referencing of InSAR measurements, will also be addressed. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026. ", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-tibethim-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Himalaya and western Tibet auxiliary data"}, "FLATSIM_TIBETHIM_INTERFEROGRAM_PUBLIC": {"abstract": "This project complements the East Tibet project of the 1st FLATSIM call, with the aim of quantifying the tectonic deformations associated with the India-Asia convergence, from the Himalayan front and across the Tibetan plateau, characterizing lithospheric rheology and hydrological, landslides and cryosphere-related processes. Possible links between the seismic cycle, external forcings and relief construction will be explored. More methodological aspects, such as the quasi-absolute referencing of InSAR measurements, will also be addressed. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-tibethim-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Himalaya and western Tibet interferograms"}, "FLATSIM_TIBETHIM_TIMESERIE_PUBLIC": {"abstract": "This project complements the East Tibet project of the 1st FLATSIM call, with the aim of quantifying the tectonic deformations associated with the India-Asia convergence, from the Himalayan front and across the Tibetan plateau, characterizing lithospheric rheology and hydrological, landslides and cryosphere-related processes. Possible links between the seismic cycle, external forcings and relief construction will be explored. More methodological aspects, such as the quasi-absolute referencing of InSAR measurements, will also be addressed. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-tibethim-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Himalaya and western Tibet time series"}, "FLATSIM_TURQUIE_AUXILIARYDATA_PUBLIC": {"abstract": "The objective of this project is to characterize the seismic and aseismic functioning of the North Anatolian and East Anatolian faults, in order to better assess their seismic hazard and understand the physical processes governing the dynamics of a fault. It also includes the monitoring of deformations in an area of \u200b\u200bgrabens in western Turkey, major geological structures with high seismic potential. This set of products provides the user with auxiliary information like informations on the processing parameters, some logs of the processing, \u2026.", "instrument": null, "keywords": "amplitude,auxiliary-data,average,burst-selection,coherence,deformation,elevation,flatsim-turquie-auxiliarydata-public,ground-to-radar,insar,lookup-tables,radar-to-ground,spectral-diversity,tectonics,unit-vector,unwrapping", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Turkey auxiliary data"}, "FLATSIM_TURQUIE_INTERFEROGRAM_PUBLIC": {"abstract": "The objective of this project is to characterize the seismic and aseismic functioning of the North Anatolian and East Anatolian faults, in order to better assess their seismic hazard and understand the physical processes governing the dynamics of a fault. It also includes the monitoring of deformations in an area of \u200b\u200bgrabens in western Turkey, major geological structures with high seismic potential. Each interferogram is embedded in an interferogram package. These packages contain Atmospheric Phase screen, wrapped and unwrapped unfiltered differential interferograms, and wrapped filtered differential interferograms, and spatial coherence.", "instrument": null, "keywords": "atmospheric-phase-screen,coherence,deformation,flatsim-turquie-interferogram-public,ground-geometry,insar,interferogram,radar-geometry,tectonics,unwrapped,wrapped", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Turkey interferograms"}, "FLATSIM_TURQUIE_TIMESERIE_PUBLIC": {"abstract": "The objective of this project is to characterize the seismic and aseismic functioning of the North Anatolian and East Anatolian faults, in order to better assess their seismic hazard and understand the physical processes governing the dynamics of a fault. It also includes the monitoring of deformations in an area of \u200b\u200bgrabens in western Turkey, major geological structures with high seismic potential. This data cube product contains phase delay images at each time step of the time series. It is cumulative through time.", "instrument": null, "keywords": "data-cube,deformation,flatsim-turquie-timeserie-public,ground-geometry,insar,mean-los-velocity,quality-maps,radar-geometry,stack-list,tectonics,time-serie", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FLATSIM Turkey time series"}, "MUSCATE_LANDSAT_LANDSAT8_L2A": {"abstract": "The level 2A products correct the data for atmospheric effects along with a mask of clouds and their shadows, as well as a mask of water and snow. Landsat products are provided by Theia in surface reflectance (level 2A) with cloud masks, the processing being performed with the MAJA algorithm. They are orthorectified and cut on the same tiles as Sentinel-2 products.", "instrument": null, "keywords": "boa-reflectance,l2a,l8,landsat,landsat8,muscate-landsat-landsat8-l2a,n2a,reflectance,satellite-image,surface", "license": "Apache-2.0", "missionStartDate": "2013-04-11T10:13:56Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE LANDSAT8 L2A"}, "MUSCATE_Landsat57_LANDSAT5_N2A": {"abstract": "Data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow. The processing methods and the data format are similar to the LANDSAT 8 data set. However there are a few differences due to input data. A resampling to Lambert'93 projection, tiling of data similar to Sentinel2, and processing with MACSS/MAJA, using multi-temporal methods for cloud screening, cloud shadow detection, water detection as well as for the estimation of the aerosol optical thickness. Time series merge LANDSAT 5 and LANDSAT 7 data as well as LANDSAT 5 data coming from adjacent tracks. The data format is the same as for Spot4/Take5.", "instrument": null, "keywords": "boa-reflectance,l2a,l5,landsat,landsat5,muscate-landsat57-landsat5-n2a,n2a,reflectance,satellite-image,surface", "license": "Apache-2.0", "missionStartDate": "2009-01-09T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE LANDSAT5 L2A"}, "MUSCATE_Landsat57_LANDSAT7_N2A": {"abstract": "Data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow. The processing methods and the data format are similar to the LANDSAT 8 data set. However there are a few differences due to input data. A resampling to Lambert'93 projection, tiling of data similar to Sentinel2, and processing with MACSS/MAJA, using multi-temporal methods for cloud screening, cloud shadow detection, water detection as well as for the estimation of the aerosol optical thickness. Time series merge LANDSAT 5 and LANDSAT 7 data as well as LANDSAT 5 data coming from adjacent tracks. The data format is the same as for Spot4/Take5.", "instrument": null, "keywords": "boa-reflectance,l2a,l7,landsat,landsat7,muscate-landsat57-landsat7-n2a,n2a,reflectance,satellite-image,surface", "license": "Apache-2.0", "missionStartDate": "2009-01-03T10:42:49Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE LANDSAT7 L2A"}, "MUSCATE_OSO_RASTER_L3B-OSO": {"abstract": "Main characteristics of the OSO Land Cover product : Production of national maps (mainland France). Nomenclature with 17 classes (2016, 2017) and 23 classes since 2018, spatial resolution between 10 m (raster) and 20 m (vector), annual update frequency. Input data : multi-temporal optical image series with high spatial resolution (Sentinel-2). The classification raster is a single raster covering the whole French metropolitan territory. It has a spatial resolution of 10 m. It results from the processing of the complete Sentinel-2 time series of the reference year using the iota\u00b2 processing chain. A Random Forest classification model is calibrated using a training dataset derived from a combination of several national and international vector data sources (BD TOPO IGN, Corine Land Cover, Urban Atlas, R\u00e9f\u00e9rentiel Parcellaire Graphique, etc.).", "instrument": null, "keywords": "l3b,l3b-oso,muscate-oso-raster-l3b-oso,oso,raster", "license": "Apache-2.0", "missionStartDate": "2016-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE OSO RASTER"}, "MUSCATE_OSO_VECTOR_L3B-OSO": {"abstract": "Main characteristics of the OSO Land Cover product : Production of national maps (mainland France). Nomenclature with 17 classes (2016, 2017) and 23 classes since 2018, spatial resolution between 10 m (raster) and 20 m (vector), annual update frequency. Input data : multi-temporal optical image series with high spatial resolution (Sentinel-2). The Vector format is a product with a minimum collection unit of 0.1 ha derived from the 20 m raster with a procedure of regularization and a simplification of the polygons obtained. In order to preserve as much information as possible from the raster product, each polygon is characterized by a set of attributes: - The majority class, with the same nomenclature of the raster product. - The average number of cloud-free images used for classification and the standard deviation. These attributes are named validmean and validstd. - The confidence of the majority class obtained from the Random Forest classifier (value between 0 and 100). - The percentage of the area covered by each class of the classification. This percentage is calculated on the 10m raster, even if the simplified polygons are derived from the 20m raster. - The area of the polygon. - The product is clipped according to the administrative boundaries of the departments and stored in a zip archive containing the 4 files that make up the \u201cESRI Shapefile\u201d format.", "instrument": null, "keywords": "l3b,l3b-oso,muscate-oso-vector-l3b-oso,oso,vector", "license": "Apache-2.0", "missionStartDate": "2016-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE OSO VECTOR"}, "MUSCATE_PLEIADES_PLEIADES_ORTHO": {"abstract": "After the successful launch of Pleiades 1A (17 December 2011) and Pleiades 1B (1 December 2012), a Thematic Acceptance Phase (RTU) was set up by CNES, in cooperation with Airbus Defence and Space. The RTU took place over two years, from March 2012 to March 2014, with the objective of: - test THR imagery and the capabilities of Pleiades satellites (agility, stereo/tri-stereo,...) - benefit from the dedicated access policy for French institutions within the framework of the Delegation of Public Service (DSP) - thematically \u00abevaluate/validate\u00bb the value-added products and services defined through 130 thematic studies proposed by the various Working Groups. These studies covered 171 geographical sites, covering several fields (coastline, sea, cartography, geology, risks, hydrology, forestry, agriculture). - evaluate the algorithms and tools developed through the Methodological Component of the ORFEO programme. More than 650 Pleiades images representing a volume of nearly 170,000 km2 that were acquired by CNES and made available free of charge to some sixty French scientific and institutional laboratories. All images acquired within the specific framework of the RTU are considered as demonstration products for non-commercial use only.", "instrument": null, "keywords": "l1c,muscate-pleiades-pleiades-ortho,pleiades,reflectance,satellite-image,toa-reflectance", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Pleiades RTU L1C"}, "MUSCATE_PLEIADES_PLEIADES_PRIMARY": {"abstract": "After the successful launch of Pleiades 1A (17 December 2011) and Pleiades 1B (1 December 2012), a Thematic Acceptance Phase (RTU) was set up by CNES, in cooperation with Airbus Defence and Space. The RTU took place over two years, from March 2012 to March 2014, with the objective of: - test THR imagery and the capabilities of Pleiades satellites (agility, stereo/tri-stereo,...) - benefit from the dedicated access policy for French institutions within the framework of the Delegation of Public Service (DSP) - thematically \u00abevaluate/validate\u00bb the value-added products and services defined through 130 thematic studies proposed by the various Working Groups. These studies covered 171 geographical sites, covering several fields (coastline, sea, cartography, geology, risks, hydrology, forestry, agriculture). - evaluate the algorithms and tools developed through the Methodological Component of the ORFEO programme. More than 650 Pleiades images representing a volume of nearly 170,000 km2 that were acquired by CNES and made available free of charge to some sixty French scientific and institutional laboratories. All images acquired within the specific framework of the RTU are considered as demonstration products for non-commercial use only.", "instrument": null, "keywords": "l1a,muscate-pleiades-pleiades-primary,pleiades,pleiades-1a,pleiades-1b,primary,rtu,satellite-image", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Pleiades RTU L1A"}, "MUSCATE_SENTINEL2_SENTINEL2_L2A": {"abstract": "The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows. Data is processed by MAJA (before called MACCS) for THEIA land data center.", "instrument": null, "keywords": "boa-reflectance,l2a,muscate-sentinel2-sentinel2-l2a,reflectance,s2,satellite-image,sentinel,sentinel2,surface", "license": "Apache-2.0", "missionStartDate": "2015-08-18T17:42:49Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SENTINEL2 L2A"}, "MUSCATE_SENTINEL2_SENTINEL2_L3A": {"abstract": "The products of level 3A provide a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis is based on a weighted arithmetic mean of clear observations.", "instrument": null, "keywords": "boa-reflectance,l3a,muscate-sentinel2-sentinel2-l3a,reflectance,s2,satellite-image,sentinel,sentinel2,surface", "license": "Apache-2.0", "missionStartDate": "2017-07-15T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SENTINEL2 L3A"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT1_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot1-l1c,reflectance,satellite-image,spot,spot-1,spot1,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "1986-03-18T20:21:50Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT1 L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT2_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot2-l1c,reflectance,satellite-image,spot,spot-2,spot2,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "1990-02-23T08:22:06Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT2 L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT3_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot3-l1c,reflectance,satellite-image,spot,spot-3,spot3,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "1993-10-02T13:56:34Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT3 L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT4_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot4-l1c,reflectance,satellite-image,spot,spot-4,spot4,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "1998-03-27T11:19:29Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT4 L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT5_L1C": {"abstract": "The Spot World Heritage Service opened in June 2015 with the first dataset about France. Nowadays, two large areas are covered, between 1986 and 2015 : Multispectral images* covering metropolitan France and overseas, and the 8 countries of Central and West Africa from the program OSFACO (Observation Spatiale des For\u00eats d\u2019Afrique Centrale et de l\u2019Ouest).", "instrument": null, "keywords": "l1c,muscate-spotworldheritage-spot5-l1c,reflectance,satellite-image,spot,spot-5,spot5,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "2002-06-21T09:52:57Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE SPOTWORLDHERITAGE SPOT5 L1C"}, "MUSCATE_Snow_LANDSAT8_L2B-SNOW": {"abstract": "The Theia snow product indicates the snow presence or absence on the land surface every fifth day if there is no cloud. The product is distributed by Theia as a raster file (8 bits GeoTIFF) of 20 m resolution and a vector file (Shapefile polygons). Level 2 offers monotemporal data, i.e. from ortho-rectified Sentinel-2 mono-date images, expressed in surface reflectance and accompanied by a cloud mask.", "instrument": null, "keywords": "cover,cryosphere,l2b,l2b-snow,l8,landsat,landsat8,muscate-snow-landsat8-l2b-snow,presence,snow,snow-mask", "license": "Apache-2.0", "missionStartDate": "2013-04-11T10:13:56Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Snow LANDSAT8 L2B"}, "MUSCATE_Snow_MULTISAT_L3B-SNOW": {"abstract": "Level 3 snow products offers annual syntheses of snow cover duration per pixel between September 1 and August 31. The L3B SNOW maps generated on September 1 are made from Sentinel-2 and Landsat-8 data. Those generated on September 2 will only be made from Sentinel-2. 4 raster files are provided in the product : 1- the snow cover duration map (SCD), pixel values within [0-number of days] corresponding the number of snow days, 2- the date of snow disappearance (Snow Melt-Out Date), defined as the last date of the longest snow period, 3- the date of snow appearance (Snow Onset Date), defined as the first date of the longest snow period, 4- the number of clear observations (NOBS) to compute the 3 other files.", "instrument": null, "keywords": "l3b,landsat,landsat8,muscate-snow-multisat-l3b-snow,presence,sentinel,sentinel2,snow,snow-cover", "license": "Apache-2.0", "missionStartDate": "2017-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE L3B Snow"}, "MUSCATE_Snow_SENTINEL2_L2B-SNOW": {"abstract": "Theia Snow product is generated from Sentinel-2 (20m resolution, every 5 days or less) and Landsat-8 images over selected areas of the globe. The processing chain used is Let-it-snow (LIS).", "instrument": null, "keywords": "cover,cryosphere,l2b,l2b-snow,muscate-snow-sentinel2-l2b-snow,presence,s2,sentinel2,snow,snow-mask", "license": "Apache-2.0", "missionStartDate": "2015-08-18T17:42:49Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Snow SENTINEL2 L2B"}, "MUSCATE_Spirit_SPOT5_1A": {"abstract": "SPOT 5 stereoscopic survey of polar ice. The objectives of the SPIRIT project were to build a large archive of Spot 5 HRS images of polar ice and, for certain regions, to produce digital terrain models (DEMs) and high-resolution images for free distribution to the community. . scientist. The target areas were the coastal regions of Greenland and Antarctica as well as all other glacial masses (Alaska, Iceland, Patagonia, etc.) surrounding the Arctic Ocean and Antarctica. The SPIRIT project made it possible to generate an archive of DEMs at 40m planimetric resolution from the HRS instrument.", "instrument": null, "keywords": "1a,dem,glacier,ice,muscate-spirit-spot5-1a,spirit,spot,spot5", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE Spirit SPOT5 L1A"}, "MUSCATE_VENUSVM05_VM5_L1C": {"abstract": "The L1C product contains 2 files : one with the metadata giving information on image acquisition (Instrument, date and time\u2013 projection and geographic coverage\u2013 Solar and viewing angles), and the second with the TOA (Top Of Atmosphere) reflectances for the 12 channels, and 3 masks (saturated pixel mask - channel 13, bad pixel mask - channel 14, and cloudy pixels - channel 15).", "instrument": null, "keywords": "l1c,muscate-venusvm05-vm5-l1c,reflectance,satellite-image,toa-reflectance,venus,vm5", "license": "Apache-2.0", "missionStartDate": "2022-03-09T11:42:13Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM5 L1C"}, "MUSCATE_VENUSVM05_VM5_L2A": {"abstract": "The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows. Data is processed by MAJA for THEIA land data center.", "instrument": null, "keywords": "boa-reflectance,l2a,muscate-venusvm05-vm5-l2a,reflectance,satellite-image,surface,venus,vm5", "license": "Apache-2.0", "missionStartDate": "2022-03-09T11:42:13Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM5 L2A"}, "MUSCATE_VENUSVM05_VM5_L3A": {"abstract": "The products of level 3A provide a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis is based on a weighted arithmetic mean of clear observations. The data processing is produced by WASP (Weighted Average Synthesis Processor)", "instrument": null, "keywords": "boa-reflectance,l3a,muscate-venusvm05-vm5-l3a,reflectance,synthesis,venus,vm5", "license": "Apache-2.0", "missionStartDate": "2023-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM5 L3A"}, "MUSCATE_VENUS_VM1_L1C": {"abstract": "The L1C product contains 2 files : one with the metadata giving information on image acquisition (Instrument, date and time\u2013 projection and geographic coverage\u2013 Solar and viewing angles), and the second with the TOA (Top Of Atmosphere) reflectances for the 12 channels, and 3 masks (saturated pixel mask - channel 13, bad pixel mask - channel 14, and cloudy pixels - channel 15).", "instrument": null, "keywords": "l1c,muscate-venus-vm1-l1c,reflectance,satellite-image,toa-reflectance,venus,vm1", "license": "Apache-2.0", "missionStartDate": "2017-11-01T10:06:54Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM1 L1C"}, "MUSCATE_VENUS_VM1_L2A": {"abstract": "The level 2A products correct the data for atmospheric effects and detect the clouds and their shadows. Data is processed by MAJA for THEIA land data center.", "instrument": null, "keywords": "boa-reflectance,l2a,muscate-venus-vm1-l2a,reflectance,satellite-image,surface,venus,vm1", "license": "Apache-2.0", "missionStartDate": "2017-11-01T10:06:54Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM1 L2A"}, "MUSCATE_VENUS_VM1_L3A": {"abstract": "The products of level 3A provide a monthly synthesis of surface reflectances from Theia's L2A products. The synthesis is based on a weighted arithmetic mean of clear observations. The data processing is produced by WASP (Weighted Average Synthesis Processor)", "instrument": null, "keywords": "boa-reflectance,l3a,muscate-venus-vm1-l3a,reflectance,synthesis,venus,vm1", "license": "Apache-2.0", "missionStartDate": "2017-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE VENUS VM1 L3A"}, "MUSCATE_WaterQual_SENTINEL2_L2B-WATER": {"abstract": "The processing chain outputs rasters of the concentration of SPM estimated in the Bands B4 and B8. The concentration is given in mg/L. So a pixel value of 21.34 corresponds to 21.34 mg/L estimated at this point. The value -10000 signifies that there is no- or invalid data available. The concentration is always calculated only over the pixels classified as water. An RGB raster for the given ROI is also included. The values correspond to reflectance TOC (Top-Of-Canopy), which is unitless. Several masks generated by the Temporal-Synthesis are also included.", "instrument": null, "keywords": "l2b,l2b-water,muscate-waterqual-sentinel2-l2b-water,s2,sentinel,sentinel2,sentinel2a,sentinel2b", "license": "Apache-2.0", "missionStartDate": "2016-01-10T10:30:07Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MUSCATE WaterQual SENTINEL2 L2B"}, "PEPS_S1_L1": {"abstract": "Sentinel-1 Level-1 products are the baseline products for the majority of users from which higher levels are derived. From data in each acquisition mode, the Instrument Processing Facility (IPF) generates focused Level-1 Single Look Complex (SLC) products and Level-1 Ground Range Detected (GRD) products. SAR parameters that vary with the satellite position in orbit, such as azimuth FM rate, Doppler centroid frequency and terrain height, are periodically updated to ensure the homogeneity of the scene when processing a complete data take. Similarly, products generated from WV data can contain any number of vignettes, potentially up to an entire orbit's worth. All Level-1 products are geo-referenced and time tagged with zero Doppler time at the centre of the swath. Geo-referencing is corrected for the azimuth bi-static bias by taking into account the pulse travel time delta between the centre of the swath and the range of each geo-referenced point. A Level-1 product can be one of the following two types: Single Look Complex (SLC) products or Ground Range Detected (GRD) products Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range. Level-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.", "instrument": null, "keywords": "backscatter,csar,grd,imagingradars,level1,peps-s1-l1,s1,sar,sentinel-1,sentinel1,slc", "license": "Apache-2.0", "missionStartDate": "2014-06-15T03:44:44.792Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "PEPS Sentinel-1 Level1"}, "PEPS_S1_L2": {"abstract": "Sentinel-1 Level-2 consists of geolocated geophysical products derived from Level-1. There is only one standard Level-2 product for wind, wave and currents applications - the Level-2 Ocean (OCN) product. The OCN product may contain the following geophysical components derived from the SAR data: - Ocean Wind field (OWI) - Ocean Swell spectra (OSW) - Surface Radial Velocity (RVL). OCN products are generated from all four Sentinel-1 imaging modes. From SM mode, the OCN product will contain all three components. From IW and EW modes, the OCN product will only contain OWI and RVL. From WV modes, the OCN product will only contain OSW and RVL.", "instrument": null, "keywords": "csar,oceans,oceanswellspectra,oceanwindfield,ocn,peps-s1-l2,s1,sar,sentinel1,surfaceradialvelocity,wavedirection,waveheight,wavelength,waveperiod,windstress", "license": "Apache-2.0", "missionStartDate": "2014-12-30T13:31:51.933Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "PEPS Sentinel-1 Level2"}, "PEPS_S2_L1C": {"abstract": "Sentinel-2 L1C tiles acquisition and storage from PEPS. Data are provided per S2 tile.", "instrument": null, "keywords": "l1c,peps-s2-l1c,reflectance,s2,sentinel2,toareflectance", "license": "Apache-2.0", "missionStartDate": "2015-07-04T10:10:06.027Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "PEPS Sentinel-2 L1C tiles"}, "PEPS_S2_L2A": {"abstract": "Sentinel-2 L2A tiles acquisition and storage from PEPS. Data are provdided per S2 tile.", "instrument": null, "keywords": "cloudcover,l2a,peps-s2-l2a,reflectance,s2,sentinel2,surfacereflectance", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "PEPS Sentinel-2 L2A tiles"}, "PEPS_S3_L1": {"abstract": "Sea surface topography measurements to at least the level of quality of the ENVISAT altimetry system, including an along track SAR capability of CRYOSAT heritage for improved measurement quality in coastal zones and over sea-ice", "instrument": null, "keywords": "altimetry,l1,level1,peps-s3-l1,s3,sentinel3,sral,ssh,stm,swh,windspeed", "license": "Apache-2.0", "missionStartDate": "2022-04-20T18:46:06.819Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GDH Sentinel-3 L1 STM Level-1 products"}, "POSTEL_LANDCOVER_GLOBCOVER": {"abstract": "A land cover map associates to each pixel of the surface a labelling characterizing the surface (ex : deciduous forest, agriculture area, etc) following a predefined nomenclature. A commonly used nomenclature is the LCCS (Land Cover Classification System) used by FAO and UNEP, and comprising 22 classes. POSTEL produces and makes available the global land cover map at 300 m resolution of the GLOBCOVER / ESA project, which can be viewed with a zooming capacity. Regional maps are also available with classes adapted to each bioclimatic area.", "instrument": null, "keywords": "classification,land-cover,land-surface,postel,postel-landcover-globcover", "license": "Apache-2.0", "missionStartDate": "2004-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL global land cover"}, "POSTEL_RADIATION_BRDF": {"abstract": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The Bidirectional Reflectance Distribution Function (FDRB) describes how terrestrial surfaces reflect the sun radiation. Its potential has been demonstrated for several applications in land surface studies (see Bicheron and Leroy, 2000). The space-borne POLDER-1/ADEOS-1 instrument (November 1996 \u2013 June 1997) has provided the first opportunity to sample the BRDF of every point on Earth for viewing angles up to 60\u00b0-70\u00b0, and for the full azimuth range, at a spatial resolution of about 6km, when the atmospheric conditions are favorable (Hautecoeur et Leroy, 1998). From April to October 2003, the land surface BRDF was sampled by the POLDER-2/ADEOS-2 sensor. From March 2005, the POLDER-3 sensor onboard the PARASOL microsatellite measures the bi-driectional reflectance of the continental ecosystems. These successive observations allowed building : 1- a BRDF database from the 8 months of POLDER-1 mesurements : The POLDER-1 BRDF data base compiles 24,857 BRDFs acquired by ADEOS-1/POLDER-1 during 8 months, from November, 1996 to June, 1997, on a maximum number of sites describing the natural variability of continental ecosystems, at several seasons whenever possible. The POLDER-1 bidirectional reflectances have been corrected from atmospheric effects using the advanced Level 2 algorithms developed for the processing line of the ADEOS-2/POLDER-2 data. The BRDF database has been implemented on the basis of the 22 vegetation classes of the GLC2000. 2- a BRDF database from the 7 months of POLDER-2 measurements : The POLDER-2 BRDF data base compiles 24,090 BRDFs acquired by ADEOS-2/POLDER-2 from April ro October 2003, on a maximum number of sites describing the natural variability of continental ecosystems, at several seasons whenever possible. The POLDER-2 bidirectional reflectances have been corrected from atmospheric effects using the advanced Level 2 algorithms described on the CNES scientific Web site. The BRDF database has been implemented on the basis of the 22 vegetation classes of the GLC2000 land cover map. 3- 4 BRDF databases from one year of POLDER-3 measurements :The LSCE, one of the POSTEL Expertise Centre, defined a new method to select the BRDFs from POLDER-3/PARASOL data acquired from November 2005 to October 2006 in order to build 4 BRDF databases. 2 MONTHLY databases gathering the best quality BRDFs for each month, independently : one based upon the IGBP land cover map, the second based upon the GLC2000 land cover map. 2 YEARLY databases designed to monitor the annual cycle of surface reflectance and its directional signature. The selection of high quality pixels is based on the full year. The first database is based upon the IGBP land cover map, the second one is based upon the GLC2000 land cover map.", "instrument": null, "keywords": "bidirectional-reflectance-distribution-function,brdf,land,land-surface,polder,postel,postel-radiation-brdf,radiation,reflectance", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Radiation BRDF"}, "POSTEL_RADIATION_DLR": {"abstract": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The Downwelling Longwave Radiation (W.m-2) (DLR) is defined as the thermal irradiance reaching the surface in the thermal infrared spectrum (4 \u2013 100 \u00b5m). It is determined by the radiation that originates from a shallow layer close to the surface, about one third being emitted by the lowest 10 meters and 80% by the 500-meter layer. The DLR is derived from several sensors (Meteosat, MSG) using various approaches, in the framework of the Geoland project.", "instrument": null, "keywords": "geoland,irradiance,land,land-surface,long-wave-radiation-descending-flux,longwave,postel,postel-radiation-dlr,radiation,thermal", "license": "Apache-2.0", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Downwelling Longwave Radiation"}, "POSTEL_RADIATION_SURFACEALBEDO": {"abstract": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The albedo is the fraction of the incoming solar radiation reflected by the land surface, integrated over the whole viewing directions. The albedo can be directional (calculated for a given sun zenith angle, also called \u201cblack-sky albedo\u201d) or hemispheric (integrated over all illumination directions, also called \u201cwhite-sky albedo\u201d), spectral (for each narrow band of the sensor) or broadband (integrated over the solar spectrum). The surface albedos are derived from many sensors (Vegetation, Polder, Meteosat) in the frame of different projects, namely Geoland and Amma.", "instrument": null, "keywords": "albedo,amma,bio,geoland,land-surface-albedo,polder,postel,postel-radiation-surfacealbedo,radiation,surface", "license": "Apache-2.0", "missionStartDate": "1996-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Radiation Surface Albedo"}, "POSTEL_RADIATION_SURFACEREFLECTANCE": {"abstract": "The \u201cradiation\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The surface reflectance is defined as the part of solar radiation reflected by the land surface. The measured surface reflectance depends on the sun zenith angle and on the viewing angular configuration. Consequently, two successive measurements of the surface reflectance cannot be directly compared. Therefore, the directional effects have to be removed using a normalization algorithm before generating a composite. The surface reflectance is provided in the frame of projects: Cyclopes, Geoland and Globcover.", "instrument": null, "keywords": "boa-reflectance,land,parasol,polder,postel,postel-radiation-surfacereflectance,radiation,reflectance,surface,surface-reflectance", "license": "Apache-2.0", "missionStartDate": "1996-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Radiation Surface Reflectance"}, "POSTEL_VEGETATION_FAPAR": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. POSTEL Vegetation FAPAR is defined as the fraction of photosynthetically active radiation absorbed by vegetation for photosynthesis activity. The FAPAR can be instantaneous or daily. FAPAR is assessed using various approaches and algorithms applied to many sensors (Vegetation, Polder, Modis, AVHRR) in the frame of Polder and Amma projects.", "instrument": null, "keywords": "amma,bio,biosphere,fapar,fraction-of-absorbed-photosynthetically-active-radiation,geoland,polder,postel,postel-vegetation-fapar,vegetation", "license": "Apache-2.0", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation FAPAR"}, "POSTEL_VEGETATION_FCOVER": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. POSTEL Vegetation FCover is the fraction of ground surface covered by vegetation. Fcover is assessed using various approaches and algorithms applied to Vegetation, and Polder, data in the frame of the Cyclopes project.", "instrument": null, "keywords": "bio,biosphere,cyclopes,fcover,geoland,postel,postel-vegetation-fcover,vegetation,vegetation-cover-fraction", "license": "Apache-2.0", "missionStartDate": "1981-08-25T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation FCover"}, "POSTEL_VEGETATION_LAI": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. POSTEL Vegetation LAI is defined as half the total foliage area per unit of ground surface (Chen and Black, 1992). It is assessed using various approaches and algorithms applied to many sensors data (Vegetation, Polder, Modis, AVHRR) in the frame of the Amma project.", "instrument": null, "keywords": "amma,bio,biosphere,geoland,lai,leaf-area-index,postel,postel-vegetation-lai,vegetation", "license": "Apache-2.0", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation LAI"}, "POSTEL_VEGETATION_NDVI": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. Postel Vegetation NDVI (Normalized Difference Vegetation Index) is calculated as the normalized ratio of the difference between the reflectances measured in the red and near-infrared sensor bands. The NDVI is the most frequently used vegetation index to assess the quantity of vegetation on the surface, and to monitor the temporal ecosystems variations. Postel provides NDVI, derived from observations of various sensors (Polder, AVHRR, Seviri) in the frame of different projects : Polder \u2013 Parasol and Amma.", "instrument": null, "keywords": "amma,bio,biosphere,ndvi,normalized-difference-vegetation-index,parasol,postel,postel-vegetation-ndvi,vegetation", "license": "Apache-2.0", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation NDVI"}, "POSTEL_VEGETATION_SURFACEREFLECTANCE": {"abstract": "The \u201ccontinental vegetation and soils\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. The surface reflectance is defined as the part of solar radiation reflected by the land surface. The measured surface reflectance depends on the sun zenith angle and on the viewing angular configuration. Consequently, two successive measurements of the surface reflectance cannot be directly compared. Therefore, the directional effects have to be removed using a normalization algorithm before generating a composite. The surface reflectance is provided in the frame of projects: Cyclopes, Geoland and Globcover.", "instrument": null, "keywords": "boa-reflectance,cyclopes,geoland,globcover,land,postel,postel-vegetation-surfacereflectance,reflectance,surface,surface-reflectance,vegetation", "license": "Apache-2.0", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Vegetation Surface Reflectance"}, "POSTEL_WATER_PRECIP": {"abstract": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, IMP (University of Vienna) and EARS assess the precipitation amount from geo-stationnary sensors images using various approaches for applications of the Observatory Natural Carbon (ONC) and of the Observatory Food Security and Crop Monitoring (OFM). Postel Water PRECIP is global scale daily precipitation product based on existing multi-satellite products and bias-corrected precipitation gauge analyses.", "instrument": null, "keywords": "athmosphere,geoland,postel,postel-water-precip,precipitation,water", "license": "Apache-2.0", "missionStartDate": "1997-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Water Precipitation"}, "POSTEL_WATER_SOILMOISTURE": {"abstract": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, University of Bonn assess soil moisture parameters from passive micro-wave sensors measurements. Postel Water Soil Moisture is water column in mm, in the upper meter of soil.", "instrument": null, "keywords": "geoland,humidity,moisture,postel,postel-water-soilmoisture,soil,water,water-surface", "license": "Apache-2.0", "missionStartDate": "2003-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Water Soil Moisture"}, "POSTEL_WATER_SURFWET": {"abstract": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, Vienna University of Technology (IPF) assess soil moisture parameters from active micro-wave sensors measurements. Postel SurfWet (Surface Wetness) is Soil moisture content in the 1-5 centimetre layer of the soil in relative units ranging between 0 wetness and total water capacity.", "instrument": null, "keywords": "geoland,postel,postel-water-surfwet,soil,soil-moisture,surface,surface-wetness,surfwet,water,water-surface", "license": "Apache-2.0", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Water Surface wet"}, "POSTEL_WATER_SWI": {"abstract": "The \u201cwater cycle\u201d biogeophysical products of Postel are spatialized variables derived from optical or micro-wave sensors measurements acquired over many years at regional to global scales. In the framework of the GEOLAND project, Vienna University of Technology (IPF) assess soil moisture parameters from active micro-wave sensors measurements. Postel Water SWI (Soil Water Index) is soil moisture content in the 1st meter of the soil in relative units ranging between wilting level and field capacity.", "instrument": null, "keywords": "geoland,humidity,moisture,postel,postel-water-swi,soil,soil-water-index,swi,water,water-surface", "license": "Apache-2.0", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "POSTEL Water Soil Water Index"}, "SWH_SPOT123_L1": {"abstract": "The SWH 1A product corresponds to the historical SPOT scene 1A product using the DIMAP format (GeoTIFF + XML metadata).\n\nFirst radiometric corrections of distortions due to differences in sensitivity of the elementary detectors of the viewing instrument. No geometric corrections.\n\n60 km x 60 km image product", "instrument": null, "keywords": "biosphere,clouds,earth-observation-satellites,glacier,habitat,lake,river,satellite-image,swh-spot123-l1,vegetation,volcano", "license": "Apache-2.0", "missionStartDate": "1986-02-23T08:53:16Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SWH SPOT1-2-3 Level1A"}, "SWH_SPOT4_L1": {"abstract": "The SWH 1A product corresponds to the historical SPOT scene 1A product using the DIMAP format (GeoTIFF + XML metadata).\n\nFirst radiometric corrections of distortions due to differences in sensitivity of the elementary detectors of the viewing instrument. No geometric corrections.\n\n60 km x 60 km image product", "instrument": null, "keywords": "biosphere,clouds,earth-observation-satellites,glacier,habitat,lake,river,satellite-image,swh-spot4-l1,vegetation,volcano", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SWH SPOT4 Level1A"}, "SWH_SPOT5_L1": {"abstract": "The SWH 1A product corresponds to the historical SPOT scene 1A product using the DIMAP format (GeoTIFF + XML metadata).\n\nFirst radiometric corrections of distortions due to differences in sensitivity of the elementary detectors of the viewing instrument. No geometric corrections.\n\n60 km x 60 km image product", "instrument": null, "keywords": "biosphere,clouds,earth-observation-satellites,glacier,habitat,lake,river,satellite-image,swh-spot5-l1,vegetation,volcano", "license": "Apache-2.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SWH SPOT5 Level1A"}, "TAKE5_SPOT4_L1C": {"abstract": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. In 2012, CESBIO seized the opportunity to set up the Take 5 experiment at the end of SPOT4\u2032s life : this experiment used SPOT4 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. On January 29, SPOT4\u2019s orbit was lowered by 3 kilometers to put it on a 5 day repeat cycle orbit. On this new orbit, the satellite will flew over the same places on earth every 5 days. Spot4 followed this orbit until June the 19th, 2013. During this period, 45 sites have been observed every 5 days, with the same repetitivity as Sentinel-2. Take5 Spot4 L1C products are data orthorectified reflectance at the top of the atmosphere.", "instrument": null, "keywords": "image,l1c,reflectance,satellite,spot,spot-4,spot4,take5,take5-spot4-l1c,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "2013-01-31T01:57:43Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TAKE5 SPOT4 LEVEL1C"}, "TAKE5_SPOT4_L2A": {"abstract": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. In 2012, CESBIO seized the opportunity to set up the Take 5 experiment at the end of SPOT4\u2032s life : this experiment used SPOT4 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. On January 29, SPOT4\u2019s orbit was lowered by 3 kilometers to put it on a 5 day repeat cycle orbit. On this new orbit, the satellite will flew over the same places on earth every 5 days. Spot4 followed this orbit until June the 19th, 2013. During this period, 45 sites have been observed every 5 days, with the same repetitivity as Sentinel-2. Take5 Spot4 L2A are data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow.", "instrument": null, "keywords": "boa-reflectance,image,l2a,satellite,spot,spot-4,spot4,surface-reflectance,take5,take5-spot4-l2a", "license": "Apache-2.0", "missionStartDate": "2013-01-31T07:07:32Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TAKE5 SPOT4 LEVEL2A"}, "TAKE5_SPOT5_L1C": {"abstract": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. Based on the success of SPOT4 (Take5), CNES decided to renew the Take5 experiment: : this experiment used SPOT5 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. This experiment started on April the 8th and lasts 5 months until September the 8th. This time, 150 sites will be observed. Take5 Spot5 L1C products are data orthorectified reflectance at the top of the atmosphere.", "instrument": null, "keywords": "image,l1c,reflectance,satellite,spot,spot5,take5,take5-spot5-l1c,toa-reflectance", "license": "Apache-2.0", "missionStartDate": "2015-04-08T00:31:16Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TAKE5 SPOT5 LEVEL1C"}, "TAKE5_SPOT5_L2A": {"abstract": "At the end of life of each satellite, CNES issues a call for ideas for short-term experiments taking place before de-orbiting the satellite. Based on the success of SPOT4 (Take5), CNES decided to renew the Take5 experiment: : this experiment used SPOT5 as a simulator of the time series that ESA\u2019s Sentinel-2 mission will provide. This experiment started on April the 8th and lasts 5 months until September the 8th. This time, 150 sites will be observed. Take5 Spot5 L2A are data ortho-rectified surface reflectance after atmospheric correction, along with a mask of clouds and their shadows, as well as a mask of water and snow.", "instrument": null, "keywords": "boa-reflectance,image,l2a,reflectance,satellite,spot,spot5,take5,take5-spot5-l2a", "license": "Apache-2.0", "missionStartDate": "2015-04-08T00:31:16Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TAKE5 SPOT5 LEVEL2A"}}, "providers_config": {"FLATSIM_AFAR_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_AFAR_AUXILIARYDATA_PUBLIC"}, "FLATSIM_AFAR_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_AFAR_INTERFEROGRAM_PUBLIC"}, "FLATSIM_AFAR_TIMESERIE_PUBLIC": {"productType": "FLATSIM_AFAR_TIMESERIE_PUBLIC"}, "FLATSIM_ANDES_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_ANDES_AUXILIARYDATA_PUBLIC"}, "FLATSIM_ANDES_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_ANDES_INTERFEROGRAM_PUBLIC"}, "FLATSIM_ANDES_TIMESERIE_PUBLIC": {"productType": "FLATSIM_ANDES_TIMESERIE_PUBLIC"}, "FLATSIM_BALKANS_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_BALKANS_AUXILIARYDATA_PUBLIC"}, "FLATSIM_BALKANS_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_BALKANS_INTERFEROGRAM_PUBLIC"}, "FLATSIM_BALKANS_TIMESERIE_PUBLIC": {"productType": "FLATSIM_BALKANS_TIMESERIE_PUBLIC"}, "FLATSIM_CAUCASE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_CAUCASE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_CAUCASE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_CAUCASE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_CAUCASE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_CAUCASE_TIMESERIE_PUBLIC"}, "FLATSIM_CHILI_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_CHILI_AUXILIARYDATA_PUBLIC"}, "FLATSIM_CHILI_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_CHILI_INTERFEROGRAM_PUBLIC"}, "FLATSIM_CHILI_TIMESERIE_PUBLIC": {"productType": "FLATSIM_CHILI_TIMESERIE_PUBLIC"}, "FLATSIM_CORSE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_CORSE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_CORSE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_CORSE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_CORSE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_CORSE_TIMESERIE_PUBLIC"}, "FLATSIM_FRANCE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_FRANCE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_FRANCE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_FRANCE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_FRANCE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_FRANCE_TIMESERIE_PUBLIC"}, "FLATSIM_INDE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_INDE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_INDE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_INDE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_INDE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_INDE_TIMESERIE_PUBLIC"}, "FLATSIM_LEVANT_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_LEVANT_AUXILIARYDATA_PUBLIC"}, "FLATSIM_LEVANT_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_LEVANT_INTERFEROGRAM_PUBLIC"}, "FLATSIM_LEVANT_TIMESERIE_PUBLIC": {"productType": "FLATSIM_LEVANT_TIMESERIE_PUBLIC"}, "FLATSIM_MAGHREB_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_MAGHREB_AUXILIARYDATA_PUBLIC"}, "FLATSIM_MAGHREB_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_MAGHREB_INTERFEROGRAM_PUBLIC"}, "FLATSIM_MAGHREB_TIMESERIE_PUBLIC": {"productType": "FLATSIM_MAGHREB_TIMESERIE_PUBLIC"}, "FLATSIM_MAKRAN_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_MAKRAN_AUXILIARYDATA_PUBLIC"}, "FLATSIM_MAKRAN_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_MAKRAN_INTERFEROGRAM_PUBLIC"}, "FLATSIM_MAKRAN_TIMESERIE_PUBLIC": {"productType": "FLATSIM_MAKRAN_TIMESERIE_PUBLIC"}, "FLATSIM_MEXIQUE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_MEXIQUE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_MEXIQUE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_MEXIQUE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_MEXIQUE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_MEXIQUE_TIMESERIE_PUBLIC"}, "FLATSIM_MOZAMBIQUE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_MOZAMBIQUE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_MOZAMBIQUE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_MOZAMBIQUE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_MOZAMBIQUE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_MOZAMBIQUE_TIMESERIE_PUBLIC"}, "FLATSIM_OKAVANGO_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_OKAVANGO_AUXILIARYDATA_PUBLIC"}, "FLATSIM_OKAVANGO_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_OKAVANGO_INTERFEROGRAM_PUBLIC"}, "FLATSIM_OKAVANGO_TIMESERIE_PUBLIC": {"productType": "FLATSIM_OKAVANGO_TIMESERIE_PUBLIC"}, "FLATSIM_OZARK_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_OZARK_AUXILIARYDATA_PUBLIC"}, "FLATSIM_OZARK_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_OZARK_INTERFEROGRAM_PUBLIC"}, "FLATSIM_OZARK_TIMESERIE_PUBLIC": {"productType": "FLATSIM_OZARK_TIMESERIE_PUBLIC"}, "FLATSIM_SHOWCASE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_SHOWCASE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_SHOWCASE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_SHOWCASE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_SHOWCASE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_SHOWCASE_TIMESERIE_PUBLIC"}, "FLATSIM_TARIM_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_TARIM_AUXILIARYDATA_PUBLIC"}, "FLATSIM_TARIM_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_TARIM_INTERFEROGRAM_PUBLIC"}, "FLATSIM_TARIM_TIMESERIE_PUBLIC": {"productType": "FLATSIM_TARIM_TIMESERIE_PUBLIC"}, "FLATSIM_TIANSHAN_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_TIANSHAN_AUXILIARYDATA_PUBLIC"}, "FLATSIM_TIANSHAN_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_TIANSHAN_INTERFEROGRAM_PUBLIC"}, "FLATSIM_TIANSHAN_TIMESERIE_PUBLIC": {"productType": "FLATSIM_TIANSHAN_TIMESERIE_PUBLIC"}, "FLATSIM_TIBETHIM_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_TIBETHIM_AUXILIARYDATA_PUBLIC"}, "FLATSIM_TIBETHIM_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_TIBETHIM_INTERFEROGRAM_PUBLIC"}, "FLATSIM_TIBETHIM_TIMESERIE_PUBLIC": {"productType": "FLATSIM_TIBETHIM_TIMESERIE_PUBLIC"}, "FLATSIM_TURQUIE_AUXILIARYDATA_PUBLIC": {"productType": "FLATSIM_TURQUIE_AUXILIARYDATA_PUBLIC"}, "FLATSIM_TURQUIE_INTERFEROGRAM_PUBLIC": {"productType": "FLATSIM_TURQUIE_INTERFEROGRAM_PUBLIC"}, "FLATSIM_TURQUIE_TIMESERIE_PUBLIC": {"productType": "FLATSIM_TURQUIE_TIMESERIE_PUBLIC"}, "MUSCATE_LANDSAT_LANDSAT8_L2A": {"productType": "MUSCATE_LANDSAT_LANDSAT8_L2A"}, "MUSCATE_Landsat57_LANDSAT5_N2A": {"productType": "MUSCATE_Landsat57_LANDSAT5_N2A"}, "MUSCATE_Landsat57_LANDSAT7_N2A": {"productType": "MUSCATE_Landsat57_LANDSAT7_N2A"}, "MUSCATE_OSO_RASTER_L3B-OSO": {"productType": "MUSCATE_OSO_RASTER_L3B-OSO"}, "MUSCATE_OSO_VECTOR_L3B-OSO": {"productType": "MUSCATE_OSO_VECTOR_L3B-OSO"}, "MUSCATE_PLEIADES_PLEIADES_ORTHO": {"productType": "MUSCATE_PLEIADES_PLEIADES_ORTHO"}, "MUSCATE_PLEIADES_PLEIADES_PRIMARY": {"productType": "MUSCATE_PLEIADES_PLEIADES_PRIMARY"}, "MUSCATE_SENTINEL2_SENTINEL2_L2A": {"productType": "MUSCATE_SENTINEL2_SENTINEL2_L2A"}, "MUSCATE_SENTINEL2_SENTINEL2_L3A": {"productType": "MUSCATE_SENTINEL2_SENTINEL2_L3A"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT1_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT1_L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT2_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT2_L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT3_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT3_L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT4_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT4_L1C"}, "MUSCATE_SPOTWORLDHERITAGE_SPOT5_L1C": {"productType": "MUSCATE_SPOTWORLDHERITAGE_SPOT5_L1C"}, "MUSCATE_Snow_LANDSAT8_L2B-SNOW": {"productType": "MUSCATE_Snow_LANDSAT8_L2B-SNOW"}, "MUSCATE_Snow_MULTISAT_L3B-SNOW": {"productType": "MUSCATE_Snow_MULTISAT_L3B-SNOW"}, "MUSCATE_Snow_SENTINEL2_L2B-SNOW": {"productType": "MUSCATE_Snow_SENTINEL2_L2B-SNOW"}, "MUSCATE_Spirit_SPOT5_1A": {"productType": "MUSCATE_Spirit_SPOT5_1A"}, "MUSCATE_VENUSVM05_VM5_L1C": {"productType": "MUSCATE_VENUSVM05_VM5_L1C"}, "MUSCATE_VENUSVM05_VM5_L2A": {"productType": "MUSCATE_VENUSVM05_VM5_L2A"}, "MUSCATE_VENUSVM05_VM5_L3A": {"productType": "MUSCATE_VENUSVM05_VM5_L3A"}, "MUSCATE_VENUS_VM1_L1C": {"productType": "MUSCATE_VENUS_VM1_L1C"}, "MUSCATE_VENUS_VM1_L2A": {"productType": "MUSCATE_VENUS_VM1_L2A"}, "MUSCATE_VENUS_VM1_L3A": {"productType": "MUSCATE_VENUS_VM1_L3A"}, "MUSCATE_WaterQual_SENTINEL2_L2B-WATER": {"productType": "MUSCATE_WaterQual_SENTINEL2_L2B-WATER"}, "PEPS_S1_L1": {"productType": "PEPS_S1_L1"}, "PEPS_S1_L2": {"productType": "PEPS_S1_L2"}, "PEPS_S2_L1C": {"productType": "PEPS_S2_L1C"}, "PEPS_S2_L2A": {"productType": "PEPS_S2_L2A"}, "PEPS_S3_L1": {"productType": "PEPS_S3_L1"}, "POSTEL_LANDCOVER_GLOBCOVER": {"productType": "POSTEL_LANDCOVER_GLOBCOVER"}, "POSTEL_RADIATION_BRDF": {"productType": "POSTEL_RADIATION_BRDF"}, "POSTEL_RADIATION_DLR": {"productType": "POSTEL_RADIATION_DLR"}, "POSTEL_RADIATION_SURFACEALBEDO": {"productType": "POSTEL_RADIATION_SURFACEALBEDO"}, "POSTEL_RADIATION_SURFACEREFLECTANCE": {"productType": "POSTEL_RADIATION_SURFACEREFLECTANCE"}, "POSTEL_VEGETATION_FAPAR": {"productType": "POSTEL_VEGETATION_FAPAR"}, "POSTEL_VEGETATION_FCOVER": {"productType": "POSTEL_VEGETATION_FCOVER"}, "POSTEL_VEGETATION_LAI": {"productType": "POSTEL_VEGETATION_LAI"}, "POSTEL_VEGETATION_NDVI": {"productType": "POSTEL_VEGETATION_NDVI"}, "POSTEL_VEGETATION_SURFACEREFLECTANCE": {"productType": "POSTEL_VEGETATION_SURFACEREFLECTANCE"}, "POSTEL_WATER_PRECIP": {"productType": "POSTEL_WATER_PRECIP"}, "POSTEL_WATER_SOILMOISTURE": {"productType": "POSTEL_WATER_SOILMOISTURE"}, "POSTEL_WATER_SURFWET": {"productType": "POSTEL_WATER_SURFWET"}, "POSTEL_WATER_SWI": {"productType": "POSTEL_WATER_SWI"}, "SWH_SPOT123_L1": {"productType": "SWH_SPOT123_L1"}, "SWH_SPOT4_L1": {"productType": "SWH_SPOT4_L1"}, "SWH_SPOT5_L1": {"productType": "SWH_SPOT5_L1"}, "TAKE5_SPOT4_L1C": {"productType": "TAKE5_SPOT4_L1C"}, "TAKE5_SPOT4_L2A": {"productType": "TAKE5_SPOT4_L2A"}, "TAKE5_SPOT5_L1C": {"productType": "TAKE5_SPOT5_L1C"}, "TAKE5_SPOT5_L2A": {"productType": "TAKE5_SPOT5_L2A"}}}, "planetary_computer": {"product_types_config": {"3dep-lidar-classification": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm). See [LAS specification](https://www.ogc.org/standards/LAS) for details.\n\nThis COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html) to select a subset of interesting classifications. Do note that not all LiDAR collections contain a full compliment of classification labels.\nTo remove outliers, the PDAL pipeline uses a noise filter and then outputs the Classification dimension.\n\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets) and [`classification`](https://github.com/stac-extensions/classification) extensions. These classes are displayed in the \"Item assets\" below. You can programmatically access the full list of class values and descriptions using the `classification:classes` field form the `data` asset on the STAC collection.\n\nClassification rasters were produced as a subset of LiDAR classification categories:\n\n```\n0, Never Classified\n1, Unclassified\n2, Ground\n3, Low Vegetation\n4, Medium Vegetation\n5, High Vegetation\n6, Building\n9, Water\n10, Rail\n11, Road\n17, Bridge Deck\n```\n", "instrument": null, "keywords": "3dep,3dep-lidar-classification,classification,cog,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Classification"}, "3dep-lidar-copc": {"abstract": "This collection contains source data from the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program) reformatted into the [COPC](https://copc.io) format. A COPC file is a LAZ 1.4 file that stores point data organized in a clustered octree. It contains a VLR that describes the octree organization of data that are stored in LAZ 1.4 chunks. The end product is a one-to-one mapping of LAZ to UTM-reprojected COPC files.\n\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud) (LPC) content stored in the compressed [LASzip](https://laszip.org?) format. Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io) organization for use in Planetary Computer, which supports incremental spatial access and cloud streaming.\n\nLPC can be summarized for construction of digital terrain models (DTM), filtered for extraction of features like vegetation and buildings, and visualized to provide a point cloud map of the physical spaces the laser scanner interacted with. LPC content from 3DEP is used to compute and extract a variety of landscape characterization products, and some of them are provided by Planetary Computer, including Height Above Ground, Relative Intensity Image, and DTM and Digital Surface Models.\n\nThe LAZ tiles represent a one-to-one mapping of original tiled content as provided by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with the exception that the data were reprojected and normalized into appropriate UTM zones for their location without adjustment to the vertical datum. In some cases, vertical datum description may not match actual data values, especially for pre-2010 USGS 3DEP point cloud data.\n\nIn addition to these COPC files, various higher-level derived products are available as Cloud Optimized GeoTIFFs in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).", "instrument": null, "keywords": "3dep,3dep-lidar-copc,cog,point-cloud,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Point Cloud"}, "3dep-lidar-dsm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "keywords": "3dep,3dep-lidar-dsm,cog,dsm,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Digital Surface Model"}, "3dep-lidar-dtm": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) to output a collection of Cloud Optimized GeoTIFFs.\n\nThe Simple Morphological Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).", "instrument": null, "keywords": "3dep,3dep-lidar-dtm,cog,dtm,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Digital Terrain Model"}, "3dep-lidar-dtm-native": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using the vendor provided (native) ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.", "instrument": null, "keywords": "3dep,3dep-lidar-dtm-native,cog,dtm,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Digital Terrain Model (Native)"}, "3dep-lidar-hag": {"abstract": "This COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc) data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\n\nThe Height Above Ground Nearest Neighbor filter takes as input a point cloud with Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround, that contains the normalized height values.\n\nGround points may be generated with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf) or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf), but you can use any method you choose, as long as the ground returns are marked.\n\nNormalized heights are a commonly used attribute of point cloud data. This can also be referred to as height above ground (HAG) or above ground level (AGL) heights. In the end, it is simply a measure of a point's relative height as opposed to its raw elevation value.\n\nThe filter finds the number of ground points nearest to the non-ground point under consideration. It calculates an average ground height weighted by the distance of each ground point from the non-ground point. The HeightAboveGround is the difference between the Z value of the non-ground point and the interpolated ground height.\n", "instrument": null, "keywords": "3dep,3dep-lidar-hag,cog,elevation,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Height above Ground"}, "3dep-lidar-intensity": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the pulse return magnitude.\n\nThe values are based on the Intensity [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "keywords": "3dep,3dep-lidar-intensity,cog,intensity,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Intensity"}, "3dep-lidar-pointsourceid": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the file source ID from which the point originated. Zero indicates that the point originated in the current file.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "keywords": "3dep,3dep-lidar-pointsourceid,cog,pointsourceid,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Point Source"}, "3dep-lidar-returns": {"abstract": "This collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the number of returns for a given pulse.\n\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\n\nThe values are based on the NumberOfReturns [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.", "instrument": null, "keywords": "3dep,3dep-lidar-returns,cog,numberofreturns,usgs", "license": "proprietary", "missionStartDate": "2012-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Lidar Returns"}, "3dep-seamless": {"abstract": "U.S.-wide digital elevation data at horizontal resolutions ranging from one to sixty meters.\n\nThe [USGS 3D Elevation Program (3DEP) Datasets](https://www.usgs.gov/core-science-systems/ngp/3dep) from the [National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map) are the primary elevation data product produced and distributed by the USGS. The 3DEP program provides raster elevation data for the conterminous United States, Alaska, Hawaii, and the island territories, at a variety of spatial resolutions. The seamless DEM layers produced by the 3DEP program are updated frequently to integrate newly available, improved elevation source data. \n\nDEM layers are available nationally at grid spacings of 1 arc-second (approximately 30 meters) for the conterminous United States, and at approximately 1, 3, and 9 meters for parts of the United States. Most seamless DEM data for Alaska is available at a resolution of approximately 60 meters, where only lower resolution source data exist.\n", "instrument": null, "keywords": "3dep,3dep-seamless,dem,elevation,ned,usgs", "license": "PDDL-1.0", "missionStartDate": "1925-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS 3DEP Seamless DEMs"}, "alos-dem": {"abstract": "The \"ALOS World 3D-30m\" (AW3D30) dataset is a 30 meter resolution global digital surface model (DSM), developed by the Japan Aerospace Exploration Agency (JAXA). AWD30 was constructed from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) on board Advanced Land Observing Satellite (ALOS), operated from 2006 to 2011.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v3.2_product_e_e1.2.pdf) for more details.\n", "instrument": "prism", "keywords": "alos,alos-dem,dem,dsm,elevation,jaxa,prism", "license": "proprietary", "missionStartDate": "2016-12-07T00:00:00Z", "platform": null, "platformSerialIdentifier": "alos", "processingLevel": null, "title": "ALOS World 3D-30m"}, "alos-fnf-mosaic": {"abstract": "The global 25m resolution SAR mosaics and forest/non-forest maps are free and open annual datasets generated by [JAXA](https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm) using the L-band Synthetic Aperture Radar sensors on the Advanced Land Observing Satellite-2 (ALOS-2 PALSAR-2), the Advanced Land Observing Satellite (ALOS PALSAR) and the Japanese Earth Resources Satellite-1 (JERS-1 SAR).\n\nThe global forest/non-forest maps (FNF) were generated by a Random Forest machine learning-based classification method, with the re-processed global 25m resolution [PALSAR-2 mosaic dataset](https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic) (Ver. 2.0.0) as input. Here, the \"forest\" is defined as the tree covered land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance with the FAO definition of forest. The classification results are presented in four categories, with two categories of forest areas: forests with a canopy cover of 90 % or more and forests with a canopy cover of 10 % to 90 %, depending on the density of the forest area.\n\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf) for more details.\n", "instrument": "PALSAR,PALSAR-2", "keywords": "alos,alos-2,alos-fnf-mosaic,forest,global,jaxa,land-cover,palsar,palsar-2", "license": "proprietary", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "title": "ALOS Forest/Non-Forest Annual Mosaic"}, "alos-palsar-mosaic": {"abstract": "Global 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)", "instrument": "PALSAR,PALSAR-2", "keywords": "alos,alos-2,alos-palsar-mosaic,global,jaxa,palsar,palsar-2,remote-sensing", "license": "proprietary", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "ALOS,ALOS-2", "processingLevel": null, "title": "ALOS PALSAR Annual Mosaic"}, "aster-l1t": {"abstract": "The [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument, launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution. ASTER images provide information about land surface temperature, color, elevation, and mineral composition.\n\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/) data from 2000-2006. L1T images have been terrain-corrected and rotated to a north-up UTM projection. Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "aster", "keywords": "aster,aster-l1t,global,nasa,satellite,terra,usgs", "license": "proprietary", "missionStartDate": "2000-03-04T12:00:00Z", "platform": null, "platformSerialIdentifier": "terra", "processingLevel": null, "title": "ASTER L1T"}, "chesapeake-lc-13": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of 13 land cover classes, although not all classes are used in all areas. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf) and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-13,land-cover", "license": "proprietary", "missionStartDate": "2013-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chesapeake Land Cover (13-class)"}, "chesapeake-lc-7": {"abstract": "A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \n\nThe dataset is composed of a uniform set of 7 land cover classes. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf). Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lc-7,land-cover", "license": "proprietary", "missionStartDate": "2013-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chesapeake Land Cover (7-class)"}, "chesapeake-lu": {"abstract": "A high-resolution 1-meter [land use data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-use-data-project/) in raster format for the entire Chesapeake Bay watershed. The dataset was created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) using 13 ancillary datasets including data on zoning, land use, parcel boundaries, landfills, floodplains, and wetlands. The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions that leads and directs Chesapeake Bay restoration efforts.\n\nThe dataset is composed of 17 land use classes in Virginia and 16 classes in all other jurisdictions. Additional information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": null, "keywords": "chesapeake-bay-watershed,chesapeake-conservancy,chesapeake-lu,land-use", "license": "proprietary", "missionStartDate": "2013-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chesapeake Land Use"}, "chloris-biomass": {"abstract": "The Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems. It covers the period 2003 - 2019, at annual time steps. The global dataset has a circa 4.6 km spatial resolution.\n\nThe maps and data sets were generated by combining multiple remote sensing measurements from space borne satellites, processed using state-of-the-art machine learning and statistical methods, validated with field data from multiple countries. The dataset provides direct estimates of aboveground stock and change, and are not based on land use or land cover area change, and as such they include gains and losses of carbon stock in all types of woody vegetation - whether natural or plantations.\n\nAnnual stocks are expressed in units of tons of biomass. Annual changes in stocks are expressed in units of CO2 equivalent, i.e., the amount of CO2 released from or taken up by terrestrial ecosystems for that specific pixel.\n\nThe spatial data sets are available on [Microsoft\u2019s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass) under a Creative Common license of the type Attribution-Non Commercial-Share Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\n\n[Chloris Geospatial](https://chloris.earth/) is a mission-driven technology company that develops software and data products on the state of natural capital for use by business, governments, and the social sector.\n", "instrument": null, "keywords": "biomass,carbon,chloris,chloris-biomass,modis", "license": "CC-BY-NC-SA-4.0", "missionStartDate": "2003-07-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Chloris Biomass"}, "cil-gdpcir-cc-by": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n| Modeling institution | Source model | Available experiments | License collection |\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\n| CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM | INM-CM4-8 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| BCC | BCC-CSM2-MR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\n| CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\n| CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40 |\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40 |\n| MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 |\n| MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40 |\n| NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| NOAA-GFDL | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40 |\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-AerChem | ssp370 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\n| CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3] |\n\n*Notes:*\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* **FGOALS-g3**\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* **INM-CM4-8**\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* **INM-CM5-0**\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* **ACCESS-CM2**\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* **ACCESS-ESM1-5**\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* **BCC-CSM2-MR**\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* **CMCC-CM2-SR5**\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* **CMCC-ESM2**\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* **EC-Earth3-AerChem**\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* **EC-Earth3-CC**\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* **EC-Earth3-Veg-LR**\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* **EC-Earth3-Veg**\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* **EC-Earth3**\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* **GFDL-CM4**\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* **GFDL-ESM4**\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* **HadGEM3-GC31-LL**\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* **MIROC-ES2L**\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* **MIROC6**\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* **MPI-ESM1-2-HR**\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* **MPI-ESM1-2-LR**\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* **NESM3**\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* **NorESM2-LM**\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* **NorESM2-MM**\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* **UKESM1-0-LL**\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n* **CanESM5**\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "keywords": "cil-gdpcir-cc-by,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-4.0", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)"}, "cil-gdpcir-cc-by-sa": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n* [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 179MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n| Modeling institution | Source model | Available experiments | License collection |\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\n| CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM | INM-CM4-8 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| BCC | BCC-CSM2-MR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40] |\n| CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40] |\n| CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40] |\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40] |\n| MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40] |\n| MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40] |\n| NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| NOAA-GFDL | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40] |\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\n| NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40] |\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40] |\n| EC-Earth-Consortium | EC-Earth3-AerChem | ssp370 | CC-BY-40] |\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40] |\n| EC-Earth-Consortium | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40] |\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40] |\n| CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-SA-40] |\n\n*Notes:*\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* **FGOALS-g3**\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* **INM-CM4-8**\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* **INM-CM5-0**\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* **ACCESS-CM2**\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* **ACCESS-ESM1-5**\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* **BCC-CSM2-MR**\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* **CMCC-CM2-SR5**\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* **CMCC-ESM2**\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* **EC-Earth3-AerChem**\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* **EC-Earth3-CC**\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* **EC-Earth3-Veg-LR**\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* **EC-Earth3-Veg**\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* **EC-Earth3**\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* **GFDL-CM4**\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* **GFDL-ESM4**\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* **HadGEM3-GC31-LL**\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* **MIROC-ES2L**\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* **MIROC6**\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* **MPI-ESM1-2-HR**\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* **MPI-ESM1-2-LR**\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* **NESM3**\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* **NorESM2-LM**\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* **NorESM2-MM**\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* **UKESM1-0-LL**\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n#### CC-BY-SA-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this license requires citation of the source model output (included here) and requires that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\n\n* **CanESM5**\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "keywords": "cil-gdpcir-cc-by-sa,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC-BY-SA-4.0", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)"}, "cil-gdpcir-cc0": {"abstract": "The World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\n\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\n\n## Accessing the data\n\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\n\n## Data format & contents\n\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\n\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\n\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201clon\u201d coordinate extends from -179.875 to 179.875, and the \u201clat\u201d coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\n\n## Available institutions, models, and scenarios by license collection\n\n| Modeling institution | Source model | Available experiments | License collection |\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\n| CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM | INM-CM4-8 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\n| BCC | BCC-CSM2-MR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\n| CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\n| CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40 |\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40 |\n| MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 |\n| MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40 |\n| NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| NOAA-GFDL | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40 |\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\n| NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-AerChem | ssp370 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\n| CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3] |\n\n*Notes:*\n\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\n\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\n\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \n\n## Project methods\n\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\n\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\n\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\n\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\n\n\n## Citing, licensing, and using data produced by this project\n\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\n\n### CIL GDPCIR\n\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\n\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \n\nThe code repository may be cited using the following:\n\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\n\n### ERA5\n\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\n\n> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\n>\n> Mu\u00f1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n>\n> Mu\u00f1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\n\n### GCM-specific citations & licenses\n\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\n\n#### Public Domain Datasets\n\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\n\n* **FGOALS-g3**\n\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\n\n CMIP Citation:\n\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\n\n ScenarioMIP Citation:\n\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\n\n\n* **INM-CM4-8**\n\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\n\n\n* **INM-CM5-0**\n\n License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\n\n CMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\n\n ScenarioMIP Citation:\n\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\n\n\n#### CC-BY-4.0\n\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\n\n* **ACCESS-CM2**\n\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\n\n CMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\n\n ScenarioMIP Citation:\n\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\n\n\n* **ACCESS-ESM1-5**\n\n License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\n\n CMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\n\n ScenarioMIP Citation:\n\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\n\n\n* **BCC-CSM2-MR**\n\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\n\n CMIP Citation:\n\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\n\n ScenarioMIP Citation:\n\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\n\n\n* **CMCC-CM2-SR5**\n\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\n\n\n* **CMCC-ESM2**\n\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\n\n CMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\n\n ScenarioMIP Citation:\n\n > Lovato, Tomas; Peano, Daniele; Butensch\u00f6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\n\n\n* **EC-Earth3-AerChem**\n\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\n\n\n* **EC-Earth3-CC**\n\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\n\n\n* **EC-Earth3-Veg-LR**\n\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\n\n\n* **EC-Earth3-Veg**\n\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\n\n\n* **EC-Earth3**\n\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\n\n CMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\n\n ScenarioMIP Citation:\n\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\n\n\n* **GFDL-CM4**\n\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\n\n CMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\n\n ScenarioMIP Citation:\n\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\n\n\n* **GFDL-ESM4**\n\n License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\n\n CMIP Citation:\n\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\n\n ScenarioMIP Citation:\n\n > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\n\n\n* **HadGEM3-GC31-LL**\n\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\n\n CMIP Citation:\n\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\n\n ScenarioMIP Citation:\n\n > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\n\n\n* **MIROC-ES2L**\n\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\n\n CMIP Citation:\n\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\n\n ScenarioMIP Citation:\n\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\n\n\n* **MIROC6**\n\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\n\n CMIP Citation:\n\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\n\n ScenarioMIP Citation:\n\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\n\n\n* **MPI-ESM1-2-HR**\n\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\n\n CMIP Citation:\n\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\n\n ScenarioMIP Citation:\n\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\u00fch, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\n\n\n* **MPI-ESM1-2-LR**\n\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\n\n CMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\n\n ScenarioMIP Citation:\n\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\u00f6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\u00fcller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\n\n\n* **NESM3**\n\n License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\n\n CMIP Citation:\n\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\n\n ScenarioMIP Citation:\n\n > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\n\n\n* **NorESM2-LM**\n\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\n\n CMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\n\n ScenarioMIP Citation:\n\n > Seland, \u00d8yvind; Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\n\n\n* **NorESM2-MM**\n\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\n\n CMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\n\n ScenarioMIP Citation:\n\n > Bentsen, Mats; Olivi\u00e8, Dirk Jan Leo; Seland, \u00d8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\u00e5g, Alf; Schwinger, J\u00f6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\n\n\n* **UKESM1-0-LL**\n\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\n\n CMIP Citation:\n\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\n\n ScenarioMIP Citation:\n\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\n\n\n* **CanESM5**\n\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\n\n CMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\n\n ScenarioMIP Citation:\n\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\n\n## Acknowledgements\n\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\n\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\n\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\n\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\n\n## Financial support\n\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\n\n## Additional links:\n\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\n* Climate Impact Lab homepage: [impactlab.org](https://impactlab.org)", "instrument": null, "keywords": "cil-gdpcir-cc0,climate-impact-lab,cmip6,precipitation,rhodium-group,temperature", "license": "CC0-1.0", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CIL Global Downscaled Projections for Climate Impacts Research (CC0-1.0)"}, "conus404": {"abstract": "[CONUS404](https://www.usgs.gov/data/conus404-four-kilometer-long-term-regional-hydroclimate-reanalysis-over-conterminous-united) is a unique, high-resolution hydro-climate dataset appropriate for forcing hydrological models and conducting meteorological analysis over the conterminous United States. CONUS404, so named because it covers the CONterminous United States for over 40 years at 4 km resolution, was produced by the Weather Research and Forecasting (WRF) model simulations run by NCAR as part of a collaboration with the USGS Water Mission Area. The CONUS404 includes 42 years of data (water years 1980-2021) and the spatial domain extends beyond the CONUS into Canada and Mexico, thereby capturing transboundary river basins and covering all contributing areas for CONUS surface waters.\n\nThe CONUS404 dataset, produced using WRF version 3.9.1.1, is the successor to the CONUS1 dataset in [ds612.0](https://rda.ucar.edu/datasets/ds612.0/) (Liu, et al., 2017) with improved representation of weather and climate conditions in the central United States due to the addition of a shallow groundwater module and several other improvements in the NOAH-Multiparameterization land surface model. It also uses a more up-to-date and higher-resolution reanalysis dataset (ERA5) as input and covers a longer period than CONUS1.", "instrument": null, "keywords": "climate,conus404,hydroclimate,hydrology,inland-waters,precipitation,weather", "license": "CC-BY-4.0", "missionStartDate": "1979-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "CONUS404"}, "cop-dem-glo-30": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 30 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: \n\n", "instrument": null, "keywords": "cop-dem-glo-30,copernicus,dem,dsm,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-30"}, "cop-dem-glo-90": {"abstract": "The Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\n\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 90 meters.\n\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\n\nSee the dataset page on OpenTopography: \n\n", "instrument": null, "keywords": "cop-dem-glo-90,copernicus,dem,elevation,tandem-x", "license": "proprietary", "missionStartDate": "2021-04-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "tandem-x", "processingLevel": null, "title": "Copernicus DEM GLO-90"}, "daymet-annual-hi": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "keywords": "climate,daymet,daymet-annual-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-07-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Annual Hawaii"}, "daymet-annual-na": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "keywords": "climate,daymet,daymet-annual-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-07-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Annual North America"}, "daymet-annual-pr": {"abstract": "Annual climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1852](https://doi.org/10.3334/ORNLDAAC/1852) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#annual). \n\n", "instrument": null, "keywords": "climate,daymet,daymet-annual-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-07-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Annual Puerto Rico"}, "daymet-daily-hi": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "keywords": "daymet,daymet-daily-hi,hawaii,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "missionStartDate": "1980-01-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Daily Hawaii"}, "daymet-daily-na": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "keywords": "daymet,daymet-daily-na,north-america,precipitation,temperature,vapor-pressure,weather", "license": "proprietary", "missionStartDate": "1980-01-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Daily North America"}, "daymet-daily-pr": {"abstract": "Gridded estimates of daily weather parameters. [Daymet](https://daymet.ornl.gov) Version 4 variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1840](https://doi.org/10.3334/ORNLDAAC/1840) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#daily).\n\n", "instrument": null, "keywords": "daymet,daymet-daily-pr,precipitation,puerto-rico,temperature,vapor-pressure,weather", "license": "proprietary", "missionStartDate": "1980-01-01T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Daily Puerto Rico"}, "daymet-monthly-hi": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "keywords": "climate,daymet,daymet-monthly-hi,hawaii,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-01-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Monthly Hawaii"}, "daymet-monthly-na": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "keywords": "climate,daymet,daymet-monthly-na,north-america,precipitation,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-01-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Monthly North America"}, "daymet-monthly-pr": {"abstract": "Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\n\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\n\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\n\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\n", "instrument": null, "keywords": "climate,daymet,daymet-monthly-pr,precipitation,puerto-rico,temperature,vapor-pressure", "license": "proprietary", "missionStartDate": "1980-01-16T12:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Daymet Monthly Puerto Rico"}, "deltares-floods": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level. At the coastline, the model is forced by extreme water levels containing surge and tide from GTSMip6. The water level at the coastline is extended landwards to all areas that are hydrodynamically connected to the coast following a \u2018bathtub\u2019 like approach and calculates the flood depth as the difference between the water level and the topography. Unlike a simple 'bathtub' model, this model attenuates the water level over land with a maximum attenuation factor of 0.5\u2009m\u2009km-1. The attenuation factor simulates the dampening of the flood levels due to the roughness over land.\n\nIn its current version, the model does not account for varying roughness over land and permanent water bodies such as rivers and lakes, and it does not account for the compound effects of waves, rainfall, and river discharge on coastal flooding. It also does not include the mitigating effect of coastal flood protection. Flood extents must thus be interpreted as the area that is potentially exposed to flooding without coastal protection.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf) for more information.\n\n## Digital elevation models (DEMs)\n\nThis documentation will refer to three DEMs:\n\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem) product.\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/), derived from SRTM and AW3D.\n* `LIDAR` is the [Global LiDAR Lowland DTM (GLL_DTM_v1)](https://data.mendeley.com/datasets/v5x4vpnzds/1).\n\n## Global datasets\n\nThis collection includes multiple global flood datasets derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different resolutions. Not all DEMs have all resolutions:\n\n* `NASADEM` and `MERITDEM` are available at `90m` and `1km` resolutions\n* `LIDAR` is available at `5km` resolution\n\n## Historic event datasets\n\nThis collection also includes historical storm event data files that follow similar DEM and resolution conventions. Not all storms events are available for each DEM and resolution combination, but generally follow the format of:\n\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\n\nFor example, a flood map for the MERITDEM-derived 90m flood data for the \"Omar\" storm in 2008 is available at:\n\n\n\n## Contact\n\nFor questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=deltares-floods%20question).", "instrument": null, "keywords": "deltares,deltares-floods,flood,global,sea-level-rise,water", "license": "CDLA-Permissive-1.0", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Deltares Global Flood Maps"}, "deltares-water-availability": {"abstract": "[Deltares](https://www.deltares.nl/en/) has produced a hydrological model approach to simulate historical daily reservoir variations for 3,236 locations across the globe for the period 1970-2020 using the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/) model. The model outputs long-term daily information on reservoir volume, inflow and outflow dynamics, as well as information on upstream hydrological forcing.\n\nThey hydrological model was forced with 5 different precipitation products. Two products (ERA5 and CHIRPS) are available at the global scale, while for Europe, USA and Australia a regional product was use (i.e. EOBS, NLDAS and BOM, respectively). Using these different precipitation products, it becomes possible to assess the impact of uncertainty in the model forcing. A different number of basins upstream of reservoirs are simulated, given the spatial coverage of each precipitation product.\n\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf) for more information.\n\n## Dataset coverages\n\n| Name | Scale | Period | Number of basins |\n|--------|--------------------------|-----------|------------------|\n| ERA5 | Global | 1967-2020 | 3236 |\n| CHIRPS | Global (+/- 50 latitude) | 1981-2020 | 2951 |\n| EOBS | Europe/North Africa | 1979-2020 | 682 |\n| NLDAS | USA | 1979-2020 | 1090 |\n| BOM | Australia | 1979-2020 | 116 |\n\n## STAC Metadata\n\nThis STAC collection includes one STAC item per dataset. The item includes a `deltares:reservoir` property that can be used to query for the URL of a specific dataset.\n\n## Contact\n\nFor questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=deltares-floods%20question).", "instrument": null, "keywords": "deltares,deltares-water-availability,precipitation,reservoir,water,water-availability", "license": "CDLA-Permissive-1.0", "missionStartDate": "1970-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Deltares Global Water Availability"}, "drcog-lulc": {"abstract": "The [Denver Regional Council of Governments (DRCOG) Land Use/Land Cover (LULC)](https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project) datasets are developed in partnership with the [Babbit Center for Land and Water Policy](https://www.lincolninst.edu/our-work/babbitt-center-land-water-policy) and the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)'s Conservation Innovation Center (CIC). DRCOG LULC includes 2018 data at 3.28ft (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution covering 6,000 square miles of the Denver, Colorado region. The classification data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP) aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project) (6in resolution everywhere except the mountainous regions to the west, which are 1ft resolution).", "instrument": null, "keywords": "drcog-lulc,land-cover,land-use,naip,usda", "license": "proprietary", "missionStartDate": "2018-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Denver Regional Council of Governments Land Use Land Cover"}, "eclipse": {"abstract": "The [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/) Network is a low-cost air quality sensing network for cities and a research project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/) at Microsoft Research.\n\nProject Eclipse currently includes over 100 locations in Chicago, Illinois, USA.\n\nThis network was deployed starting in July, 2021, through a collaboration with the City of Chicago, the Array of Things Project, JCDecaux Chicago, and the Environmental Law and Policy Center as well as local environmental justice organizations in the city. [This talk]( https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/) documents the network design and data calibration strategy.\n\n## Storage resources\n\nData are stored in [Parquet](https://parquet.apache.org/) files in Azure Blob Storage in the West Europe Azure region, in the following blob container:\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\n\nWithin that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`, where `YYYY-MM-DD` corresponds to the date of the snapshot.\nEach snapshot contains a sensor readings from the next 7-days in Parquet format starting with date on the folder name YYYY-MM-DD.\nTherefore, the data files for the first snapshot are at\n\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_*.parquet\n\nThe Parquet file schema is as described below. \n\n## Additional Documentation\n\nFor details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).\n\n## License and attribution\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022) [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)\n\n## Contact\n\nFor questions about this dataset, contact [`msrurbanops@microsoft.com`](mailto:msrurbanops@microsoft.com?subject=eclipse%20question) \n\n\n## Learn more\n\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/) contains an overview of the Project Eclipse at Microsoft Research.\n\n", "instrument": null, "keywords": "air-pollution,eclipse,pm25", "license": "proprietary", "missionStartDate": "2021-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Urban Innovation Eclipse Sensor Data"}, "ecmwf-forecast": {"abstract": "The [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products) offers real-time meterological and oceanographic productions from the ECMWF forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction) for detailed information on each of the products.\n\n## Overview of products\n\nThe following diagram shows the publishing schedule of the various products.\n\n\n\nThe vertical axis shows the various products, defined below, which are grouped by combinations of `stream`, `forecast type`, and `reference time`. The horizontal axis shows *forecast times* in 3-hour intervals out from the reference time. A black square over a particular forecast time, or step, indicates that a forecast is made for that forecast time, for that particular `stream`, `forecast type`, `reference time` combination.\n\n* **stream** is the forecasting system that produced the data. The values are available in the `ecmwf:stream` summary of the STAC collection. They are:\n * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), atmospheric fields\n * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast) fields from the ECMWF model only.\n * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast), atmospheric fields \n * `scda`: short cut-off high-resolution forecast, atmospheric fields (also known as \"high-frequency products\")\n * `scwv`: short cut-off high-resolution forecast, ocean wave fields (also known as \"high-frequency products\") and\n * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), ocean wave fields,\n * `wave`: wave model\n* **type** is the forecast type. The values are available in the `ecmwf:type` summary of the STAC collection. They are:\n * `fc`: forecast\n * `ef`: ensemble forecast\n * `pf`: ensemble probabilities\n * `tf`: trajectory forecast for tropical cyclone tracks\n* **reference time** is the hours after midnight when the model was run. Each stream / type will produce assets for different forecast times (steps from the reference datetime) depending on the reference time.\n\nVisit the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) for more details on each of the various products.\n\nAssets are available for the previous 30 days.\n\n## Asset overview\n\nThe data are provided as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\nAdditionally, [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles) are provided, which can be used to read subsets of the data from Azure Blob Storage.\n\nWithin each `stream`, `forecast type`, `reference time`, the structure of the data are mostly consistent. Each GRIB2 file will have the\nsame data variables, coordinates (aside from `time` as the *reference time* changes and `step` as the *forecast time* changes). The exception\nis the `enfo-ep` and `waef-ep` products, which have more `step`s in the 240-hour forecast than in the 360-hour forecast. \n\nSee the example notebook for more on how to access the data.\n\n## STAC metadata\n\nThe Planetary Computer provides a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so every item has the same\n`bbox` and `geometry`.\n\nA few custom properties are available on each STAC item, which can be used in searches to narrow down the data to items of interest:\n\n* `ecmwf:stream`: The forecasting system (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:type`: The forecast type (see above for definitions). The full set of values is available in the Collection's summaries.\n* `ecmwf:step`: The offset from the reference datetime, expressed as ``, for example `\"3h\"` means \"3 hours from the reference datetime\". \n* `ecmwf:reference_datetime`: The datetime when the model was run. This indicates when the forecast *was made*, rather than when it's valid for.\n* `ecmwf:forecast_datetime`: The datetime for which the forecast is valid. This is also set as the item's `datetime`.\n\nSee the example notebook for more on how to use the STAC metadata to query for particular data.\n\n## Attribution\n\nThe products listed and described on this page are available to the public and their use is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that the data may be redistributed and used commercially, subject to appropriate attribution.\n\nThe following wording should be attached to the use of this ECMWF dataset: \n\n1. Copyright statement: Copyright \"\u00a9 [year] European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\n3. License Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications.\n\nThe following wording shall be attached to services created with this ECMWF dataset:\n\n1. Copyright statement: Copyright \"This service is based on data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF)\".\n2. Source www.ecmwf.int\n3. License Statement: This ECMWF data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n4. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications\n\n## More information\n\nFor more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).", "instrument": null, "keywords": "ecmwf,ecmwf-forecast,forecast,weather", "license": "CC-BY-4.0", "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ECMWF Open Data (real-time)"}, "era5-pds": {"abstract": "ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate\ncovering the period from January 1950 to present. ERA5 is produced by the\nCopernicus Climate Change Service (C3S) at ECMWF.\n\nReanalysis combines model data with observations from across the world into a\nglobally complete and consistent dataset using the laws of physics. This\nprinciple, called data assimilation, is based on the method used by numerical\nweather prediction centres, where every so many hours (12 hours at ECMWF) a\nprevious forecast is combined with newly available observations in an optimal\nway to produce a new best estimate of the state of the atmosphere, called\nanalysis, from which an updated, improved forecast is issued. Reanalysis works\nin the same way, but at reduced resolution to allow for the provision of a\ndataset spanning back several decades. Reanalysis does not have the constraint\nof issuing timely forecasts, so there is more time to collect observations, and\nwhen going further back in time, to allow for the ingestion of improved versions\nof the original observations, which all benefit the quality of the reanalysis\nproduct.\n\nThis dataset was converted to Zarr by [Planet OS](https://planetos.com/).\nSee [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\nfor more.\n\n## STAC Metadata\n\nTwo types of data variables are provided: \"forecast\" (`fc`) and \"analysis\" (`an`).\n\n* An **analysis**, of the atmospheric conditions, is a blend of observations\n with a previous forecast. An analysis can only provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters (parameters valid at a specific time, e.g temperature at 12:00),\n but not accumulated parameters, mean rates or min/max parameters.\n* A **forecast** starts with an analysis at a specific time (the 'initialization\n time'), and a model computes the atmospheric conditions for a number of\n 'forecast steps', at increasing 'validity times', into the future. A forecast\n can provide\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\n parameters, accumulated parameters, mean rates, and min/max parameters.\n\nEach [STAC](https://stacspec.org/) item in this collection covers a single month\nand the entire globe. There are two STAC items per month, one for each type of data\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties to\nindicate which kind of variables that STAC item catalogs.\n\n## How to acknowledge, cite and refer to ERA5\n\nAll users of data on the Climate Data Store (CDS) disks (using either the web interface or the CDS API) must provide clear and visible attribution to the Copernicus programme and are asked to cite and reference the dataset provider:\n\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nCite each dataset used as indicated on the relevant CDS entries (see link to \"Citation\" under References on the Overview page of the dataset entry).\n\nThroughout the content of your publication, the dataset used is referred to as Author (YYYY).\n\nThe 3-steps procedure above is illustrated with this example: [Use Case 2: ERA5 hourly data on single levels from 1979 to present](https://confluence.ecmwf.int/display/CKB/Use+Case+2%3A+ERA5+hourly+data+on+single+levels+from+1979+to+present).\n\nFor complete details, please refer to [How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it).", "instrument": null, "keywords": "ecmwf,era5,era5-pds,precipitation,reanalysis,temperature,weather", "license": "proprietary", "missionStartDate": "1979-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ERA5 - PDS"}, "esa-cci-lc": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf) to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).\n", "instrument": null, "keywords": "cci,esa,esa-cci-lc,global,land-cover", "license": "proprietary", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA Climate Change Initiative Land Cover Maps (Cloud Optimized GeoTIFF)"}, "esa-cci-lc-netcdf": {"abstract": "The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \n\nThe data in this Collection are the original NetCDF files accessed from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc), which provides the data as Cloud Optimized GeoTIFFs.", "instrument": null, "keywords": "cci,esa,esa-cci-lc-netcdf,global,land-cover", "license": "proprietary", "missionStartDate": "1992-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA Climate Change Initiative Land Cover Maps (NetCDF)"}, "esa-worldcover": {"abstract": "The European Space Agency (ESA) [WorldCover](https://esa-worldcover.org/en) product provides global land cover maps for the years 2020 and 2021 at 10 meter resolution based on the combination of [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) radar data and [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) imagery. The discrete classification maps provide 11 classes defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The map images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n\nThe WorldCover product is developed by a consortium of European service providers and research organizations. [VITO](https://remotesensing.vito.be/) (Belgium) is the prime contractor of the WorldCover consortium together with [Brockmann Consult](https://www.brockmann-consult.de/) (Germany), [CS SI](https://www.c-s.fr/) (France), [Gamma Remote Sensing AG](https://www.gamma-rs.ch/) (Switzerland), [International Institute for Applied Systems Analysis](https://www.iiasa.ac.at/) (Austria), and [Wageningen University](https://www.wur.nl/nl/Wageningen-University.htm) (The Netherlands).\n\nTwo versions of the WorldCover product are available:\n\n- WorldCover 2020 produced using v100 of the algorithm\n - [WorldCover 2020 v100 User Manual](https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf)\n - [WorldCover 2020 v100 Validation Report]()\n\n- WorldCover 2021 produced using v200 of the algorithm\n - [WorldCover 2021 v200 User Manual]()\n - [WorldCover 2021 v200 Validaton Report]()\n\nSince the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps include both changes in real land cover and changes due to the used algorithms.\n", "instrument": "c-sar,msi", "keywords": "c-sar,esa,esa-worldcover,global,land-cover,msi,sentinel,sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "license": "CC-BY-4.0", "missionStartDate": "2020-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "sentinel-1a,sentinel-1b,sentinel-2a,sentinel-2b", "processingLevel": null, "title": "ESA WorldCover"}, "fia": {"abstract": "Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service's [Forest Inventory and Analysis](https://www.fia.fs.fed.us/) (FIA) program.\n\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation's forest land.\n\nDomain: continental U.S., 1928-2018\n\nResolution: plot-level (irregular polygon)\n\nThis dataset was curated and brought to Azure by [CarbonPlan](https://carbonplan.org/).\n", "instrument": null, "keywords": "biomass,carbon,fia,forest,forest-service,species,usda", "license": "CC0-1.0", "missionStartDate": "2020-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Forest Inventory and Analysis"}, "fws-nwi": {"abstract": "The Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated.\n\n**NOTE:** Due to the variation in use and analysis of this data by the end user, each state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.\n\nFor more information, visit the National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\n\n## STAC Metadata\n\nIn addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet) asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles) to select just the geoparquet assets. See the Example Notebook for more.", "instrument": null, "keywords": "fws-nwi,united-states,usfws,wetlands", "license": "proprietary", "missionStartDate": "2022-10-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "FWS National Wetlands Inventory"}, "gap": {"abstract": "The [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5), based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states), are the foundation of the most detailed, consistent map of vegetation available for the United States. These data facilitate planning and management for biological diversity on a regional and national scale.\n\nThis dataset includes the [land cover](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover) component of the GAP/LANDFIRE project.\n\n", "instrument": null, "keywords": "gap,land-cover,landfire,united-states,usgs", "license": "proprietary", "missionStartDate": "1999-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS Gap Land Cover"}, "gbif": {"abstract": "The [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is an international network and data infrastructure funded by the world's governments, providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences.\n\nThe GBIF occurrence dataset combines data from a wide array of sources, including specimen-related data from natural history museums, observations from citizen science networks, and automated environmental surveys. While these data are constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots are taken and made available here. \n\nData are stored in [Parquet](https://parquet.apache.org/) format; the Parquet file schema is described below. Most field names correspond to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and have been interpreted by GBIF's systems to align taxonomy, location, dates, etc. Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\n\nPlease refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines) for information about how to cite GBIF data in publications.. For analyses using the whole dataset, please use the following citation:\n\n> GBIF.org ([Date]) GBIF Occurrence Data [DOI of dataset]\n\nFor analyses where data are significantly filtered, please track the datasetKeys used and use a \"[derived dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\" record for citing the data.\n\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/) contains a number of articles that can help you analyze GBIF data.\n", "instrument": null, "keywords": "biodiversity,gbif,species", "license": "proprietary", "missionStartDate": "2021-04-13T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Biodiversity Information Facility (GBIF)"}, "gnatsgo-rasters": {"abstract": "This collection contains the raster data for gNATSGO. In order to use the map unit values contained in the `mukey` raster asset, you'll need to join to tables represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables) Collection. Many items have commonly used values encoded in additional raster assets.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "keywords": "gnatsgo-rasters,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "missionStartDate": "2020-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "gNATSGO Soil Database - Rasters"}, "gnatsgo-tables": {"abstract": "This collection contains the table data for gNATSGO. This table data can be used to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters) Collection.\n\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\n\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\n\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\n\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)", "instrument": null, "keywords": "gnatsgo-tables,natsgo,rss,soils,ssurgo,statsgo2,united-states,usda", "license": "CC0-1.0", "missionStartDate": "2020-07-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "gNATSGO Soil Database - Tables"}, "goes-cmi": {"abstract": "The GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product provides 16 reflective and emissive bands at high temporal cadence over the Western Hemisphere.\n\nThe GOES-R series is the latest in the Geostationary Operational Environmental Satellites (GOES) program, which has been operated in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer. The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions between 0.5km and 2km.\n\n### Geographic coverage\n\nThe ABI captures three levels of coverage, each at a different temporal cadence depending on the modes described below. The geographic coverage for each image is described by the `goes:image-type` STAC Item property.\n\n- _FULL DISK_: a circular image depicting nearly full coverage of the Western Hemisphere.\n- _CONUS_: a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\n- _MESOSCALE_: a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between two different mesoscale geographic regions.\n\n### Modes\n\nThere are three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and Mode 6.\n\n- Mode _3_ consists of one observation of the full disk scene of the Earth, three observations of the continental United States (CONUS), and thirty observations for each of two distinct mesoscale views every fifteen minutes.\n- Mode _4_ consists of the observation of the full disk scene every five minutes.\n- Mode _6_ consists of one observation of the full disk scene of the Earth, two observations of the continental United States (CONUS), and twenty observations for each of two distinct mesoscale views every ten minutes.\n\nThe mode that each image was captured with is described by the `goes:mode` STAC Item property.\n\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8) video for an idea of how the ABI scans multiple geographic regions over time.\n\n### Cloud and Moisture Imagery\n\nThe Cloud and Moisture Imagery product contains one or more images with pixel values identifying \"brightness values\" that are scaled to support visual analysis. Cloud and Moisture Imagery product (CMIP) files are generated for each of the sixteen ABI reflective and emissive bands. In addition, there is a multi-band product file that includes the imagery at all bands (MCMIP).\n\nThe Planetary Computer STAC Collection `goes-cmi` captures both the CMIP and MCMIP product files into individual STAC Items for each observation from a GOES-R satellite. It contains the original CMIP and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of the data from each MCMIP band (2km); the full-resolution CMIP band for bands 1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful for rendering.\n\nThis product is not in a standard coordinate reference system (CRS), which can cause issues with some tooling that does not handle non-standard large geographic regions.\n\n### For more information\n- [Beginner\u2019s Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\n- [GOES-R Series Product Definition and Users\u2019 Guide: Volume 5 (Level 2A+ Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish verison](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/raw/main/GOES/QuickGuides/Spanish/Guia%20introductoria%20para%20datos%20de%20la%20serie%20GOES-R%20V1.1%20FINAL2%20-%20Copy.pdf))\n\n", "instrument": "ABI", "keywords": "abi,cloud,goes,goes-16,goes-17,goes-18,goes-cmi,moisture,nasa,noaa,satellite", "license": "proprietary", "missionStartDate": "2017-02-28T00:16:52Z", "platform": null, "platformSerialIdentifier": "GOES-16,GOES-17,GOES-18", "processingLevel": null, "title": "GOES-R Cloud & Moisture Imagery"}, "goes-glm": {"abstract": "The [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html) is a single-channel, near-infrared optical transient detector that can detect the momentary changes in an optical scene, indicating the presence of lightning. GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground) activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. GLM collects information such as the frequency, location and extent of lightning discharges to identify intensifying thunderstorms and tropical cyclones. Trends in total lightning available from the GLM provide critical information to forecasters, allowing them to focus on developing severe storms much earlier and before these storms produce damaging winds, hail or even tornadoes.\n\nThe GLM data product consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, and flashes:\n\n- Lightning events are detected by the instrument.\n- Lightning groups are a collection of one or more lightning events that satisfy temporal and spatial coincidence thresholds.\n- Similarly, lightning flashes are a collection of one or more lightning groups that satisfy temporal and spatial coincidence thresholds.\n\nThe product includes the relationship among lightning events, groups, and flashes, and the area coverage of lightning groups and flashes. The product also includes processing and data quality metadata, and satellite state and location information. \n\nThis Collection contains GLM L2 data in tabular ([GeoParquet](https://github.com/opengeospatial/geoparquet)) format and the original source NetCDF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": "FM1,FM2", "keywords": "fm1,fm2,goes,goes-16,goes-17,goes-glm,l2,lightning,nasa,noaa,satellite,weather", "license": "proprietary", "missionStartDate": "2018-02-13T16:10:00Z", "platform": "GOES", "platformSerialIdentifier": "GOES-16,GOES-17", "processingLevel": ["L2"], "title": "GOES-R Lightning Detection"}, "gpm-imerg-hhr": {"abstract": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the [GPM satellite constellation](https://gpm.nasa.gov/missions/gpm/constellation) to estimate precipitation over the majority of the Earth's surface. This algorithm is particularly valuable over the majority of the Earth's surface that lacks precipitation-measuring instruments on the ground. Now in the latest Version 06 release of IMERG the algorithm fuses the early precipitation estimates collected during the operation of the TRMM satellite (2000 - 2015) with more recent precipitation estimates collected during operation of the GPM satellite (2014 - present). The longer the record, the more valuable it is, as researchers and application developers will attest. By being able to compare and contrast past and present data, researchers are better informed to make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security.\n\nFor more, see the [IMERG homepage](https://gpm.nasa.gov/data/imerg) The [IMERG Technical documentation](https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf) provides more information on the algorithm, input datasets, and output products.", "instrument": null, "keywords": "gpm,gpm-imerg-hhr,imerg,precipitation", "license": "proprietary", "missionStartDate": "2000-06-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GPM IMERG"}, "gridmet": {"abstract": "gridMET is a dataset of daily surface meteorological data at approximately four-kilometer resolution, covering the contiguous U.S. from 1979 to the present. These data can provide important inputs for ecological, agricultural, and hydrological models.\n", "instrument": null, "keywords": "climate,gridmet,precipitation,temperature,vapor-pressure,water", "license": "CC0-1.0", "missionStartDate": "1979-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "gridMET"}, "hgb": {"abstract": "This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution. The aboveground biomass map integrates land-cover-specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover-specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree/land cover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.\n", "instrument": null, "keywords": "biomass,carbon,hgb,ornl", "license": "proprietary", "missionStartDate": "2010-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "HGB: Harmonized Global Biomass for 2010"}, "hrea": {"abstract": "The [HREA](http://www-personal.umich.edu/~brianmin/HREA/index.html) project aims to provide open access to new indicators of electricity access and reliability across the world. Leveraging satellite imagery with computational methods, these high-resolution data provide new tools to track progress toward reliable and sustainable energy access across the world.\n\nThis dataset includes settlement-level measures of electricity access, reliability, and usage for 89 nations, derived from nightly VIIRS satellite imagery. Specifically, this dataset provides the following annual values at country-level granularity:\n\n1. **Access**: Predicted likelihood that a settlement is electrified, based on night-by-night comparisons of each settlement against matched uninhabited areas over a calendar year.\n\n2. **Reliability**: Proportion of nights a settlement is statistically brighter than matched uninhabited areas. Areas with more frequent power outages or service interruptions have lower rates.\n\n3. **Usage**: Higher levels of brightness indicate more robust usage of outdoor lighting, which is highly correlated with overall energy consumption.\n\n4. **Nighttime Lights**: Annual composites of VIIRS nighttime light output.\n\nFor more information and methodology, please visit the [HREA website](http://www-personal.umich.edu/~brianmin/HREA/index.html).\n", "instrument": null, "keywords": "electricity,hrea,viirs", "license": "CC-BY-4.0", "missionStartDate": "2012-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "HREA: High Resolution Electricity Access"}, "io-biodiversity": {"abstract": "Generated by [Impact Observatory](https://www.impactobservatory.com/), in collaboration with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\n\nMaps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579) of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future.\n\nBiodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf) entitled \u201cGlobal 100m Projections of Biodiversity Intactness for the years 2017-2020.\u201d\n\nAll years are available under a Creative Commons BY-4.0 license.\n", "instrument": null, "keywords": "biodiversity,global,io-biodiversity", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Biodiversity Intactness"}, "io-lulc": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2020 data available in the newer [9-class dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)._\n\nGlobal estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution. This dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the relevant yearly Sentinel-2 scenes on the Planetary Computer.\n\nThis dataset is also available on the [ArcGIS Living Atlas of the World](https://livingatlas.arcgis.com/landcover/).\n", "instrument": null, "keywords": "global,io-lulc,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Esri 10-Meter Land Cover (10-class)"}, "io-lulc-9-class": {"abstract": "__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2023 data available in the newer [9-class v2 dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02)._\n\nTime series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "keywords": "global,io-lulc-9-class,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "10m Annual Land Use Land Cover (9-class) V1"}, "io-lulc-annual-v02": {"abstract": "Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\n\nThis dataset, produced by [Impact Observatory](http://impactobservatory.com/), Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\n\nThese maps have been improved from Impact Observatory\u2019s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class) and provide a relative reduction in the amount of anomalous change between classes, particularly between \u201cBare\u201d and any of the vegetative classes \u201cTrees,\u201d \u201cCrops,\u201d \u201cFlooded Vegetation,\u201d and \u201cRangeland\u201d. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery.\n\nAll years are available under a Creative Commons BY-4.0.", "instrument": null, "keywords": "global,io-lulc-annual-v02,land-cover,land-use,sentinel", "license": "CC-BY-4.0", "missionStartDate": "2017-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "10m Annual Land Use Land Cover (9-class) V2"}, "jrc-gsw": {"abstract": "Global surface water products from the European Commission Joint Research Centre, based on Landsat 5, 7, and 8 imagery. Layers in this collection describe the occurrence, change, and seasonality of surface water from 1984-2020. Complete documentation for each layer is available in the [Data Users Guide](https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf).\n", "instrument": null, "keywords": "global,jrc-gsw,landsat,water", "license": "proprietary", "missionStartDate": "1984-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "JRC Global Surface Water"}, "kaza-hydroforecast": {"abstract": "This dataset is a daily updated set of HydroForecast seasonal river flow forecasts at six locations in the Kwando and Upper Zambezi river basins. More details about the locations, project context, and to interactively view current and previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\n\n## Flow forecast dataset and model description\n\n[HydroForecast](https://www.upstream.tech/hydroforecast) is a theory-guided machine learning hydrologic model that predicts streamflow in basins across the world. For the Kwando and Upper Zambezi, HydroForecast makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview). The model's output is probabilistic and the mean, median and a range of quantiles are available at each forecast step.\n\nThe underlying model has the following attributes: \n\n* Timestep: 10 days\n* Horizon: 10 to 180 days \n* Update frequency: daily\n* Units: cubic meters per second (m\u00b3/s)\n \n## Site details\n\nThe model produces output for six locations in the Kwando and Upper Zambezi river basins.\n\n* Upper Zambezi sites\n * Zambezi at Chavuma\n * Luanginga at Kalabo\n* Kwando basin sites\n * Kwando at Kongola -- total basin flows\n * Kwando Sub-basin 1\n * Kwando Sub-basin 2 \n * Kwando Sub-basin 3\n * Kwando Sub-basin 4\n * Kwando Kongola Sub-basin\n\n## STAC metadata\n\nThere is one STAC item per location. Each STAC item has a single asset linking to a Parquet file in Azure Blob Storage.", "instrument": null, "keywords": "hydroforecast,hydrology,kaza-hydroforecast,streamflow,upstream-tech,water", "license": "CDLA-Sharing-1.0", "missionStartDate": "2022-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "HydroForecast - Kwando & Upper Zambezi Rivers"}, "landsat-c2-l1": {"abstract": "Landsat Collection 2 Level-1 data, consisting of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data. These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.\n\nThis dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "mss", "keywords": "global,imagery,landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-c2-l1,mss,nasa,satellite,usgs", "license": "proprietary", "missionStartDate": "1972-07-25T00:00:00Z", "platform": null, "platformSerialIdentifier": "landsat-1,landsat-2,landsat-3,landsat-4,landsat-5", "processingLevel": null, "title": "Landsat Collection 2 Level-1"}, "landsat-c2-l2": {"abstract": "Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\n\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", "instrument": "tm,etm+,oli,tirs", "keywords": "etm+,global,imagery,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2-l2,nasa,oli,reflectance,satellite,temperature,tirs,tm,usgs", "license": "proprietary", "missionStartDate": "1982-08-22T00:00:00Z", "platform": null, "platformSerialIdentifier": "landsat-4,landsat-5,landsat-7,landsat-8,landsat-9", "processingLevel": null, "title": "Landsat Collection 2 Level-2"}, "mobi": {"abstract": "The [Map of Biodiversity Importance](https://www.natureserve.org/conservation-tools/projects/map-biodiversity-importance) (MoBI) consists of raster maps that combine habitat information for 2,216 imperiled species occurring in the conterminous United States, using weightings based on range size and degree of protection to identify areas of high importance for biodiversity conservation. Species included in the project are those which, as of September 2018, had a global conservation status of G1 (critical imperiled) or G2 (imperiled) or which are listed as threatened or endangered at the full species level under the United States Endangered Species Act. Taxonomic groups included in the project are vertebrates (birds, mammals, amphibians, reptiles, turtles, crocodilians, and freshwater and anadromous fishes), vascular plants, selected aquatic invertebrates (freshwater mussels and crayfish) and selected pollinators (bumblebees, butterflies, and skippers).\n\nThere are three types of spatial data provided, described in more detail below: species richness, range-size rarity, and protection-weighted range-size rarity. For each type, this data set includes five different layers – one for all species combined, and four additional layers that break the data down by taxonomic group (vertebrates, plants, freshwater invertebrates, and pollinators) – for a total of fifteen layers.\n\nThese data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\n", "instrument": null, "keywords": "biodiversity,mobi,natureserve,united-states", "license": "proprietary", "missionStartDate": "2020-04-14T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MoBI: Map of Biodiversity Importance"}, "modis-09A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) 09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "keywords": "aqua,global,imagery,mod09a1,modis,modis-09a1-061,myd09a1,nasa,reflectance,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Surface Reflectance 8-Day (500m)"}, "modis-09Q1-061": {"abstract": "The 09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.", "instrument": "modis", "keywords": "aqua,global,imagery,mod09q1,modis,modis-09q1-061,myd09q1,nasa,reflectance,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Surface Reflectance 8-Day (250m)"}, "modis-10A1-061": {"abstract": "This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS Snow Cover 5-Min L2 Swath 500m' data set. Each data granule is a 10degx10deg tile projected to a 500 m sinusoidal grid.", "instrument": "modis", "keywords": "aqua,global,mod10a1,modis,modis-10a1-061,myd10a1,nasa,satellite,snow,terra", "license": "proprietary", "missionStartDate": "2000-02-24T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Snow Cover Daily"}, "modis-10A2-061": {"abstract": "This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS Snow Cover Daily L3 Global 500m Grid' data set. A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.", "instrument": "modis", "keywords": "aqua,global,mod10a2,modis,modis-10a2-061,myd10a2,nasa,satellite,snow,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Snow Cover 8-day"}, "modis-11A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types", "instrument": "modis", "keywords": "aqua,global,mod11a1,modis,modis-11a1-061,myd11a1,nasa,satellite,temperature,terra", "license": "proprietary", "missionStartDate": "2000-02-24T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Land Surface Temperature/Emissivity Daily"}, "modis-11A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.", "instrument": "modis", "keywords": "aqua,global,mod11a2,modis,modis-11a2-061,myd11a2,nasa,satellite,temperature,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Land Surface Temperature/Emissivity 8-Day"}, "modis-13A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "keywords": "aqua,global,mod13a1,modis,modis-13a1-061,myd13a1,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Vegetation Indices 16-Day (500m)"}, "modis-13Q1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers.", "instrument": "modis", "keywords": "aqua,global,mod13q1,modis,modis-13q1-061,myd13q1,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Vegetation Indices 16-Day (250m)"}, "modis-14A1-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition.", "instrument": "modis", "keywords": "aqua,fire,global,mod14a1,modis,modis-14a1-061,myd14a1,nasa,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Thermal Anomalies/Fire Daily"}, "modis-14A2-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MOD14A2 gridded composite contains the maximum value of the individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators.", "instrument": "modis", "keywords": "aqua,fire,global,mod14a2,modis,modis-14a2-061,myd14a2,nasa,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Thermal Anomalies/Fire 8-Day"}, "modis-15A2H-061": {"abstract": "The Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is an 8-day composite dataset with 500 meter pixel size. The algorithm chooses the best pixel available from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "keywords": "aqua,global,mcd15a2h,mod15a2h,modis,modis-15a2h-061,myd15a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2002-07-04T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Leaf Area Index/FPAR 8-Day"}, "modis-15A3H-061": {"abstract": "The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.", "instrument": "modis", "keywords": "aqua,global,mcd15a3h,modis,modis-15a3h-061,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2002-07-04T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Leaf Area Index/FPAR 4-Day"}, "modis-16A3GF-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The product will be generated at the end of each year when the entire yearly 8-day MOD15A2H/MYD15A2H is available. Hence, the gap-filled product is the improved 16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year. Provided in the product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.", "instrument": "modis", "keywords": "aqua,global,mod16a3gf,modis,modis-16a3gf-061,myd16a3gf,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2001-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Net Evapotranspiration Yearly Gap-Filled"}, "modis-17A2H-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.", "instrument": "modis", "keywords": "aqua,global,mod17a2h,modis,modis-17a2h-061,myd17a2h,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Gross Primary Productivity 8-Day"}, "modis-17A2HGF-061": {"abstract": "The Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. This product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled A2HGF is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "keywords": "aqua,global,mod17a2hgf,modis,modis-17a2hgf-061,myd17a2hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Gross Primary Productivity 8-Day Gap-Filled"}, "modis-17A3HGF-061": {"abstract": "The Version 6.1 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual Moderate Resolution Imaging Spectroradiometer (MODIS) NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled product is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.", "instrument": "modis", "keywords": "aqua,global,mod17a3hgf,modis,modis-17a3hgf-061,myd17a3hgf,nasa,satellite,terra,vegetation", "license": "proprietary", "missionStartDate": "2000-02-18T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Net Primary Production Yearly Gap-Filled"}, "modis-21A2-061": {"abstract": "A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the MOD11 LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. This dataset is an 8-day composite LST product at 1,000 meter spatial resolution that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free 21A1D and 21A1N daily acquisitions from the 8-day period. Unlike the 21A1 data sets where the daytime and nighttime acquisitions are separate products, the 21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).", "instrument": "modis", "keywords": "aqua,global,mod21a2,modis,modis-21a2-061,myd21a2,nasa,satellite,temperature,terra", "license": "proprietary", "missionStartDate": "2000-02-16T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Land Surface Temperature/3-Band Emissivity 8-Day"}, "modis-43A4-061": {"abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.", "instrument": "modis", "keywords": "aqua,global,imagery,mcd43a4,modis,modis-43a4-061,nasa,reflectance,satellite,terra", "license": "proprietary", "missionStartDate": "2000-02-16T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Nadir BRDF-Adjusted Reflectance (NBAR) Daily"}, "modis-64A1-061": {"abstract": "The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.", "instrument": "modis", "keywords": "aqua,fire,global,imagery,mcd64a1,modis,modis-64a1-061,nasa,satellite,terra", "license": "proprietary", "missionStartDate": "2000-11-01T00:00:00Z", "platform": null, "platformSerialIdentifier": "aqua,terra", "processingLevel": null, "title": "MODIS Burned Area Monthly"}, "ms-buildings": {"abstract": "Bing Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases.\n\nFor more information, see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/) repository on GitHub.\n\n## Building footprint creation\n\nThe building extraction is done in two stages:\n\n1. Semantic Segmentation \u2013 Recognizing building pixels on an aerial image using deep neural networks (DNNs)\n2. Polygonization \u2013 Converting building pixel detections into polygons\n\n**Stage 1: Semantic Segmentation**\n\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\n\n**Stage 2: Polygonization**\n\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\n\n## Data assets\n\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet) datasets in [Delta][delta] table format.\nThe data are partitioned by\n\n1. Region\n2. quadkey at [Bing Map Tiles][tiles] level 9\n\nEach `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area.\n\nNote that older items in this dataset are *not* spatially partitioned. We recommend using data with a processing date\nof 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata\nfor each item (see below).\n\n## Delta Format\n\nThe collection-level asset under the `delta` key gives you the fsspec-style URL\nto the Delta table. This can be used to efficiently query for matching partitions\nby `Region` and `quadkey`. See the notebook for an example using Python.\n\n## STAC metadata\n\nThis STAC collection has one STAC item per region. The `msbuildings:region`\nproperty can be used to filter items to a specific region, and the `msbuildings:quadkey`\nproperty can be used to filter items to a specific quadkey (though you can also search\nby the `geometry`).\n\nNote that older STAC items are not spatially partitioned. We recommend filtering on\nitems with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection\nsummary for `msbuildings:processing-date` for a list of valid values.\n\n[delta]: https://delta.io/\n[tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\n", "instrument": null, "keywords": "bing-maps,buildings,delta,footprint,geoparquet,microsoft,ms-buildings", "license": "ODbL-1.0", "missionStartDate": "2014-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Microsoft Building Footprints"}, "mtbs": {"abstract": "[Monitoring Trends in Burn Severity](https://www.mtbs.gov/) (MTBS) is an inter-agency program whose goal is to consistently map the burn severity and extent of large fires across the United States from 1984 to the present. This includes all fires 1000 acres or greater in the Western United States and 500 acres or greater in the Eastern United States. The burn severity mosaics in this dataset consist of thematic raster images of MTBS burn severity classes for all currently completed MTBS fires for the continental United States and Alaska.\n", "instrument": null, "keywords": "fire,forest,mtbs,usda,usfs,usgs", "license": "proprietary", "missionStartDate": "1984-12-31T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "MTBS: Monitoring Trends in Burn Severity"}, "naip": {"abstract": "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) \nprovides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). \nNAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) \nwithin the [US Department of Agriculture](https://www.usda.gov/) (USDA). \nData are captured at least once every three years for each state. \nThis dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\nYou can visualize the coverage of current and past collections [here](https://naip-usdaonline.hub.arcgis.com/). \n", "instrument": null, "keywords": "aerial,afpo,agriculture,imagery,naip,united-states,usda", "license": "proprietary", "missionStartDate": "2010-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NAIP: National Agriculture Imagery Program"}, "nasa-nex-gddp-cmip6": {"abstract": "The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four \u201cTier 1\u201d greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.\n\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/) maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6) where you can find more information about these datasets. Users are encouraged to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf), provided alongside the data set, where more detailed information, references and acknowledgements can be found.\n\nThis collection contains many NetCDF files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\n\n- **model** is the name of a modeling group (e.g. \"ACCESS-CM-2\"). See the `cmip6:model` summary in the STAC collection for a full list of models.\n- **scenario** is one of \"historical\", \"ssp245\" or \"ssp585\".\n- **variable** is one of \"hurs\", \"huss\", \"pr\", \"rlds\", \"rsds\", \"sfcWind\", \"tas\", \"tasmax\", \"tasmin\".\n- **year** depends on the value of *scenario*. For \"historical\", the values range from 1950 to 2014 (inclusive). For \"ssp245\" and \"ssp585\", the years range from 2015 to 2100 (inclusive).\n\nIn addition to the NetCDF files, we provide some *experimental* **reference files** as collection-level dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\nThese files include the positions of data variables within the binary NetCDF files, which can speed up reading the metadata. See the example notebook for more.", "instrument": null, "keywords": "climate,cmip6,humidity,nasa,nasa-nex-gddp-cmip6,precipitation,temperature", "license": "proprietary", "missionStartDate": "1950-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)"}, "nasadem": {"abstract": "[NASADEM](https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem) provides global topographic data at 1 arc-second (~30m) horizontal resolution, derived primarily from data captured via the [Shuttle Radar Topography Mission](https://www2.jpl.nasa.gov/srtm/) (SRTM).\n\n", "instrument": null, "keywords": "dem,elevation,jpl,nasa,nasadem,nga,srtm,usgs", "license": "proprietary", "missionStartDate": "2000-02-20T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NASADEM HGT v001"}, "noaa-c-cap": {"abstract": "Nationally standardized, raster-based inventories of land cover for the coastal areas of the U.S. Data are derived, through the Coastal Change Analysis Program, from the analysis of multiple dates of remotely sensed imagery. Two file types are available: individual dates that supply a wall-to-wall map, and change files that compare one date to another. The use of standardized data and procedures assures consistency through time and across geographies. C-CAP data forms the coastal expression of the National Land Cover Database (NLCD) and the A-16 land cover theme of the National Spatial Data Infrastructure. The data are updated every 5 years.", "instrument": null, "keywords": "coastal,land-cover,land-use,noaa,noaa-c-cap", "license": "proprietary", "missionStartDate": "1975-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "C-CAP Regional Land Cover and Change"}, "noaa-cdr-ocean-heat-content": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content,ocean,temperature", "license": "proprietary", "missionStartDate": "1972-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Heat Content CDR"}, "noaa-cdr-ocean-heat-content-netcdf": {"abstract": "The Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-ocean-heat-content`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-ocean-heat-content-netcdf,ocean,temperature", "license": "proprietary", "missionStartDate": "1972-03-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Heat Content CDR NetCDFs"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"abstract": "The NOAA 1/4\u00b0 daily Optimum Interpolation Sea Surface Temperature (or daily OISST) Climate Data Record (CDR) provides complete ocean temperature fields constructed by combining bias-adjusted observations from different platforms (satellites, ships, buoys) on a regular global grid, with gaps filled in by interpolation. The main input source is satellite data from the Advanced Very High Resolution Radiometer (AVHRR), which provides high temporal-spatial coverage from late 1981-present. This input must be adjusted to the buoys due to erroneous cold SST data following the Mt Pinatubo and El Chichon eruptions. Applications include climate modeling, resource management, ecological studies on annual to daily scales.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-optimum-interpolation-netcdf`.\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-optimum-interpolation,ocean,temperature", "license": "proprietary", "missionStartDate": "1981-09-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Surface Temperature - Optimum Interpolation CDR"}, "noaa-cdr-sea-surface-temperature-whoi": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi,ocean,temperature", "license": "proprietary", "missionStartDate": "1988-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Surface Temperature - WHOI CDR"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"abstract": "The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\u00b0 resolution grid over the global ice-free oceans from January 1988\u2014present.\n\nThis is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-sea-surface-temperature-whoi`.\nThe NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "climate,global,noaa,noaa-cdr-sea-surface-temperature-whoi-netcdf,ocean,temperature", "license": "proprietary", "missionStartDate": "1988-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Sea Surface Temperature - WHOI CDR NetCDFs"}, "noaa-climate-normals-gridded": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nThe data in this Collection have been converted from the original NetCDF format to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n## STAC Metadata\n\nThe STAC items in this collection contain several custom fields that can be used to further filter the data.\n\n* `noaa_climate_normals:period`: Climate normal time period. This can be \"1901-2000\", \"1991-2020\", or \"2006-2020\".\n* `noaa_climate_normals:frequency`: Climate normal temporal interval (frequency). This can be \"daily\", \"monthly\", \"seasonal\" , or \"annual\"\n* `noaa_climate_normals:time_index`: Time step index, e.g., month of year (1-12).\n\nThe `description` field of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions are\n\n* annual: \"Annual precipitation normals from monthly precipitation normal values\"\n* seasonal: \"Seasonal precipitation normals (WSSF) from monthly normals\"\n* monthly: \"Monthly precipitation normals from monthly precipitation values\"\n* daily: \"Precipitation normals from daily averages\"\n\nCheck the assets on individual items for the appropriate description.\n\nThe STAC keys for most assets consist of two abbreviations. A \"variable\":\n\n\n| Abbreviation | Description |\n| ------------ | ---------------------------------------- |\n| prcp | Precipitation over the time period |\n| tavg | Mean temperature over the time period |\n| tmax | Maximum temperature over the time period |\n| tmin | Minimum temperature over the time period |\n\nAnd an \"aggregation\":\n\n| Abbreviation | Description |\n| ------------ | ------------------------------------------------------------------------------ |\n| max | Maximum of the variable over the time period |\n| min | Minimum of the variable over the time period |\n| std | Standard deviation of the value over the time period |\n| flag | An count of the number of inputs (months, years, etc.) to calculate the normal |\n| norm | The normal for the variable over the time period |\n\nSo, for example, `prcp_max` for monthly data is the \"Maximum values of all input monthly precipitation normal values\".\n", "instrument": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-gridded,surface-observations,weather", "license": "proprietary", "missionStartDate": "1901-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA US Gridded Climate Normals (Cloud-Optimized GeoTIFF)"}, "noaa-climate-normals-netcdf": {"abstract": "The [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThe data in this Collection are the original NetCDF files provided by NOAA's National Centers for Environmental Information. This Collection contains gridded data for the following frequencies and time periods:\n\n- Annual, seasonal, and monthly normals\n - 100-year (1901\u20132000)\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n- Daily normals\n - 30-year (1991\u20132020)\n - 15-year (2006\u20132020)\n\nFor most use-cases, we recommend using the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded) collection, which contains the same data in Cloud Optimized GeoTIFF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-netcdf,surface-observations,weather", "license": "proprietary", "missionStartDate": "1901-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA US Gridded Climate Normals (NetCDF)"}, "noaa-climate-normals-tabular": {"abstract": "The [NOAA United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals) provide information about typical climate conditions for thousands of weather station locations across the United States. Normals act both as a ruler to compare current weather and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables for each weather station. \n\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \n\nThis Collection contains tabular weather variable data at weather station locations in GeoParquet format, converted from the source CSV files. The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\nData are provided for annual/seasonal, monthly, daily, and hourly frequencies for the following time periods:\n\n- Legacy 30-year normals (1981\u20132010)\n- Supplemental 15-year normals (2006\u20132020)\n", "instrument": null, "keywords": "climate-normals,climatology,conus,noaa,noaa-climate-normals-tabular,surface-observations,weather", "license": "proprietary", "missionStartDate": "1981-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA US Tabular Climate Normals"}, "noaa-mrms-qpe-1h-pass1": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the **1-Hour Pass 1** sub-product, i.e., 1-hour cumulative precipitation accumulation with a 1-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass1,precipitation,qpe,united-states,weather", "license": "proprietary", "missionStartDate": "2022-07-21T20:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA MRMS QPE 1-Hour Pass 1"}, "noaa-mrms-qpe-1h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the **1-Hour Pass 2** sub-product, i.e., 1-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n", "instrument": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-1h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "missionStartDate": "2022-07-21T20:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA MRMS QPE 1-Hour Pass 2"}, "noaa-mrms-qpe-24h-pass2": {"abstract": "The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\n\nMRMS QPE is available as a \"Pass 1\" or \"Pass 2\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\n\nThis Collection contains the **24-Hour Pass 2** sub-product, i.e., 24-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).", "instrument": null, "keywords": "caribbean,guam,mrms,noaa,noaa-mrms-qpe-24h-pass2,precipitation,qpe,united-states,weather", "license": "proprietary", "missionStartDate": "2022-07-21T20:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "NOAA MRMS QPE 24-Hour Pass 2"}, "noaa-nclimgrid-monthly": {"abstract": "The [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) consists of four climate variables derived from the [Global Historical Climatology Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) dataset: maximum temperature, minimum temperature, average temperature, and precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer) grids for the Continental United States (CONUS). \n\nNClimGrid data is available in monthly and daily temporal intervals, with the daily data further differentiated as \"prelim\" (preliminary) or \"scaled\". Preliminary daily data is available within approximately three days of collection. Once a calendar month of preliminary daily data has been collected, it is scaled to match the corresponding monthly value. Monthly data is available from 1895 to the present. Daily preliminary and daily scaled data is available from 1951 to the present. \n\nThis Collection contains **Monthly** data. See the journal publication [\"Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml) for more information about monthly gridded data.\n\nUsers of all NClimGrid data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) that:\n>\"On an annual basis, approximately one year of 'final' NClimGrid data is submitted to replace the initially supplied 'preliminary' data for the same time period. Users should be sure to ascertain which level of data is required for their research.\"\n\nThe source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\n\n*Note*: The Planetary Computer currently has STAC metadata for just the monthly collection. We'll have STAC metadata for daily data in our next release. In the meantime, you can access the daily NetCDF data directly from Blob Storage using the storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`. See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns for more.*\n", "instrument": null, "keywords": "climate,nclimgrid,noaa,noaa-nclimgrid-monthly,precipitation,temperature,united-states", "license": "proprietary", "missionStartDate": "1895-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Monthly NOAA U.S. Climate Gridded Dataset (NClimGrid)"}, "nrcan-landcover": {"abstract": "Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis.", "instrument": null, "keywords": "canada,land-cover,landsat,north-america,nrcan-landcover,remote-sensing", "license": "OGL-Canada-2.0", "missionStartDate": "2015-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Land Cover of Canada"}, "planet-nicfi-analytic": {"abstract": "*Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "keywords": "imagery,nicfi,planet,planet-nicfi-analytic,satellite,tropics", "license": "proprietary", "missionStartDate": "2015-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Planet-NICFI Basemaps (Analytic)"}, "planet-nicfi-visual": {"abstract": "*Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/) and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\n\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\n\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\n\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\n* Increase transparency and accountability in the tropics.\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\n\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\n\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.", "instrument": null, "keywords": "imagery,nicfi,planet,planet-nicfi-visual,satellite,tropics", "license": "proprietary", "missionStartDate": "2015-12-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Planet-NICFI Basemaps (Visual)"}, "sentinel-1-grd": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Level-1 Ground Range Detected (GRD) products in this Collection consist of focused SAR data that has been detected, multi-looked and projected to ground range using the Earth ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range (but can be different for each IW/EW sub-swath).\n\nGround range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution.\n\nFor the IW and EW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarization.\n\nFor more information see the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\n\n### Terrain Correction\n\nUsers might want to geometrically or radiometrically terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc) collection is a global radiometrically terrain corrected dataset derived from Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM). See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/) for more.", "instrument": null, "keywords": "c-band,copernicus,esa,grd,sar,sentinel,sentinel-1,sentinel-1-grd,sentinel-1a,sentinel-1b", "license": "proprietary", "missionStartDate": "2014-10-10T00:28:21Z", "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "title": "Sentinel 1 Level-1 Ground Range Detected (GRD)"}, "sentinel-1-rtc": {"abstract": "The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd) products produced by the European Space Agency. The RTC processing is performed by [Catalyst](https://catalyst.earth/).\n\nRadiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.\n\nA Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed) for more information.\n\n### Methodology\n\nThe Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/S1-Radiometric-Calibration-V1.0.pdf). The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.\n\nThe data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, [\u201cFlattening Gamma: Radiometric Terrain Correction for SAR Imagery\u201d](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf), IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.\n\nCalibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.\n\nRTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.\n", "instrument": null, "keywords": "c-band,copernicus,esa,rtc,sar,sentinel,sentinel-1,sentinel-1-rtc,sentinel-1a,sentinel-1b", "license": "CC-BY-4.0", "missionStartDate": "2014-10-10T00:28:21Z", "platform": "Sentinel-1", "platformSerialIdentifier": "SENTINEL-1A,SENTINEL-1B", "processingLevel": null, "title": "Sentinel 1 Radiometrically Terrain Corrected (RTC)"}, "sentinel-2-l2a": {"abstract": "The [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) program provides global imagery in thirteen spectral bands at 10m-60m resolution and a revisit time of approximately five days. This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using [Sen2Cor](https://step.esa.int/main/snap-supported-plugins/sen2cor/) and converted to [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.", "instrument": "msi", "keywords": "copernicus,esa,global,imagery,msi,reflectance,satellite,sentinel,sentinel-2,sentinel-2-l2a,sentinel-2a,sentinel-2b", "license": "proprietary", "missionStartDate": "2015-06-27T10:25:31Z", "platform": "sentinel-2", "platformSerialIdentifier": "Sentinel-2A,Sentinel-2B", "processingLevel": null, "title": "Sentinel-2 Level-2A"}, "sentinel-3-olci-lfr-l2-netcdf": {"abstract": "This collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2] products containing data on global vegetation, chlorophyll, and water vapor.\n\n## Data files\n\nThis dataset includes data on three primary variables:\n\n* OLCI global vegetation index file\n* terrestrial Chlorophyll index file\n* integrated water vapor over water file.\n\nEach variable is contained within a separate NetCDF file, and is cataloged as an asset in each Item.\n\nSeveral associated variables are also provided in the annotations data files:\n\n* rectified reflectance for red and NIR channels (RC681 and RC865)\n* classification, quality and science flags (LQSF)\n* common data such as the ortho-geolocation of land pixels, solar and satellite angles, atmospheric and meteorological data, time stamp or instrument information. These variables are inherited from Level-1B products.\n\nThis full resolution product offers a spatial sampling of approximately 300 m.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/land-products\n", "instrument": "OLCI", "keywords": "biomass,copernicus,esa,land,olci,sentinel,sentinel-3,sentinel-3-olci-lfr-l2-netcdf,sentinel-3a,sentinel-3b", "license": "proprietary", "missionStartDate": "2016-04-25T11:33:47.368562Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land (Full Resolution)"}, "sentinel-3-olci-wfr-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 Full Resolution [OLCI Level-2 Water][olci-l2] products containing data on water-leaving reflectance, ocean color, and more.\n\n## Data files\n\nThis dataset includes data on:\n\n- Surface directional reflectance\n- Chlorophyll-a concentration\n- Suspended matter concentration\n- Energy flux\n- Aerosol load\n- Integrated water vapor column\n\nEach variable is contained within NetCDF files. Error estimates are available for each product.\n\n## Processing overview\n\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\n\nThis Collection contains Level-2 data in NetCDF files from November 2017 to present.\n\n[olci-l2]: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/ocean-products\n", "instrument": "OLCI", "keywords": "copernicus,esa,ocean,olci,sentinel,sentinel-3,sentinel-3-olci-wfr-l2-netcdf,sentinel-3a,sentinel-3b,water", "license": "proprietary", "missionStartDate": "2017-11-01T00:07:01.738487Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Water (Full Resolution)"}, "sentinel-3-slstr-frp-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Fire Radiative Power](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) (FRP) products containing data on fires detected over land and ocean.\n\n## Data files\n\nThe primary measurement data is contained in the `FRP_in.nc` file and provides FRP and uncertainties, projected onto a 1km grid, for fires detected in the thermal infrared (TIR) spectrum over land. Since February 2022, FRP and uncertainties are also provided for fires detected in the short wave infrared (SWIR) spectrum over both land and ocean, with the delivered data projected onto a 500m grid. The latter SWIR-detected fire data is only available for night-time measurements and is contained in the `FRP_an.nc` or `FRP_bn.nc` files.\n\nIn addition to the measurement data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags.\n\n## Processing\n\nThe TIR fire detection is based on measurements from the S7 and F1 bands of the [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument); SWIR fire detection is based on the S5 and S6 bands. More information about the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\n\nThis Collection contains Level-2 data in NetCDF files from August 2020 to present.\n", "instrument": "SLSTR", "keywords": "copernicus,esa,fire,satellite,sentinel,sentinel-3,sentinel-3-slstr-frp-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "missionStartDate": "2020-08-08T23:11:15.617203Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Fire Radiative Power"}, "sentinel-3-slstr-lst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Land Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) products containing data on land surface temperature measurements on a 1km grid. Radiance is measured in two channels to determine the temperature of the Earth's surface skin in the instrument field of view, where the term \"skin\" refers to the top surface of bare soil or the effective emitting temperature of vegetation canopies as viewed from above.\n\n## Data files\n\nThe dataset includes data on the primary measurement variable, land surface temperature, in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains several ancillary variables:\n\n- Normalized Difference Vegetation Index\n- Surface biome classification\n- Fractional vegetation cover\n- Total water vapor column\n\nIn addition to the primary and ancillary data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\n\n## STAC Item geometries\n\nThe Collection contains small \"chips\" and long \"stripes\" of data collected along the satellite direction of travel. Approximately five percent of the STAC Items describing long stripes of data contain geometries that encompass a larger area than an exact concave hull of the data extents. This may require additional filtering when searching the Collection for Items that spatially intersect an area of interest.\n", "instrument": "SLSTR", "keywords": "copernicus,esa,land,satellite,sentinel,sentinel-3,sentinel-3-slstr-lst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "missionStartDate": "2016-04-19T01:35:17.188500Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land Surface Temperature"}, "sentinel-3-slstr-wst-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SLSTR Level-2 Water Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) products containing data on sea surface temperature measurements on a 1km grid. Each product consists of a single NetCDF file containing all data variables:\n\n- Sea Surface Temperature (SST) value\n- SST total uncertainty\n- Latitude and longitude coordinates\n- SST time deviation\n- Single Sensor Error Statistic (SSES) bias and standard deviation estimate\n- Contextual parameters such as wind speed at 10 m and fractional sea-ice contamination\n- Quality flag\n- Satellite zenith angle\n- Top Of Atmosphere (TOA) Brightness Temperature (BT)\n- TOA noise equivalent BT\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\n\nThis Collection contains Level-2 data in NetCDF files from October 2017 to present.\n", "instrument": "SLSTR", "keywords": "copernicus,esa,ocean,satellite,sentinel,sentinel-3,sentinel-3-slstr-wst-l2-netcdf,sentinel-3a,sentinel-3b,slstr,temperature", "license": "proprietary", "missionStartDate": "2017-10-31T23:59:57.451604Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Sea Surface Temperature"}, "sentinel-3-sral-lan-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Land Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on land radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from March 2016 to present.\n", "instrument": "SRAL", "keywords": "altimetry,copernicus,esa,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-lan-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "missionStartDate": "2016-03-01T14:07:51.632846Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land Radar Altimetry"}, "sentinel-3-sral-wat-l2-netcdf": {"abstract": "This Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on ocean radar altimetry measurements. Each product contains three NetCDF files:\n\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\n\nThis Collection contains Level-2 data in NetCDF files from January 2017 to present.\n", "instrument": "SRAL", "keywords": "altimetry,copernicus,esa,ocean,radar,satellite,sentinel,sentinel-3,sentinel-3-sral-wat-l2-netcdf,sentinel-3a,sentinel-3b,sral", "license": "proprietary", "missionStartDate": "2017-01-28T00:59:14.149496Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Ocean Radar Altimetry"}, "sentinel-3-synergy-aod-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Aerosol Optical Depth](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) product, which is a downstream development of the Sentinel-2 Level-1 [OLCI Full Resolution](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci/data-formats/level-1) and [SLSTR Radiances and Brightness Temperatures](https://sentinels.copernicus.eu/web/sentinel/user-guides/Sentinel-3-slstr/data-formats/level-1) products. The dataset provides both retrieved and diagnostic global aerosol parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF file for all regions over land and ocean free of snow/ice cover, excluding high cloud fraction data. The retrieved and derived aerosol parameters are:\n\n- Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\n- Error estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and 2250 nm\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and 2250 nm\n- Fine-mode AOD at 550nm\n- Aerosol Angstrom parameter between 550 and 865nm\n- Dust AOD at 550nm\n- Aerosol absorption optical depth at 550nm\n\nAtmospherically corrected nadir surface directional reflectances at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution are also provided. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\n\nThis Collection contains Level-2 data in NetCDF files from April 2020 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "aerosol,copernicus,esa,global,olci,satellite,sentinel,sentinel-3,sentinel-3-synergy-aod-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2020-04-16T19:36:28.012367Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Global Aerosol"}, "sentinel-3-synergy-syn-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) product, which contains data on Surface Directional Reflectance, Aerosol Optical Thickness, and an Angstrom coefficient estimate over land.\n\n## Data Files\n\nIndividual NetCDF files for the following variables:\n\n- Surface Directional Reflectance (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr) channels (S1 to S6 for both nadir and oblique views, except S4) and for all [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci) channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the water vapor bands Oa19 and Oa20.\n- Aerosol optical thickness at 550nm with error estimates.\n- Angstrom coefficient at 550nm.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "aerosol,copernicus,esa,land,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-syn-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-09-22T16:51:00.001276Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Land Surface Reflectance and Aerosol"}, "sentinel-3-synergy-v10-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from ground reflectance during a 10-day window, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 10-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\n\nThis Collection contains Level-2 data in NetCDF files from September 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-v10-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-09-27T11:17:21Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 10-Day Surface Reflectance and NDVI (SPOT VEGETATION)"}, "sentinel-3-synergy-vg1-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 1-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from daily ground reflecrtance, and four surface reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\n\n## Data files\n\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nEach 1-day product is delivered as a set of 10 rectangular scenes:\n\n- AFRICA\n- NORTH_AMERICA\n- SOUTH_AMERICA\n- CENTRAL_AMERICA\n- NORTH_ASIA\n- WEST_ASIA\n- SOUTH_EAST_ASIA\n- ASIAN_ISLANDS\n- AUSTRALASIA\n- EUROPE\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "copernicus,esa,ndvi,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vg1-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-10-04T23:17:21Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 1-Day Surface Reflectance and NDVI (SPOT VEGETATION)"}, "sentinel-3-synergy-vgp-l2-netcdf": {"abstract": "This Collection provides the Sentinel-3 [Synergy Level-2 Top of Atmosphere Reflectance](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) product, which is a SPOT VEGETATION Continuity Product containing measurement data similar to that obtained by the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboad the SPOT-3 and SPOT-4 satellites. The primary variables are four top of atmosphere reflectance bands:\n\n- B0 (Blue, 450nm)\n- B2 (Red, 645nm)\n- B3 (NIR, 835nm)\n- MIR (SWIR, 1665nm)\n\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument and have been adapted for scientific applications requiring highly accurate physical measurements through correction for systematic errors and re-sampling to predefined geographic projections. The pixel brightness count is the ground area's apparent reflectance as seen at the top of atmosphere.\n\n## Data files\n\nNetCDF files are provided for the four reflectance bands. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\n\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\n\nThis Collection contains Level-2 data in NetCDF files from October 2018 to present.\n", "instrument": "OLCI,SLSTR", "keywords": "copernicus,esa,olci,reflectance,satellite,sentinel,sentinel-3,sentinel-3-synergy-vgp-l2-netcdf,sentinel-3a,sentinel-3b,slstr", "license": "proprietary", "missionStartDate": "2018-10-08T08:09:40.491227Z", "platform": "Sentinel-3", "platformSerialIdentifier": "Sentinel-3A,Sentinel-3B", "processingLevel": null, "title": "Sentinel-3 Top of Atmosphere Reflectance (SPOT VEGETATION)"}, "sentinel-5p-l2-netcdf": {"abstract": "The Copernicus [Sentinel-5 Precursor](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5p) mission provides high spatio-temporal resolution measurements of the Earth's atmosphere. The mission consists of one satellite carrying the [TROPOspheric Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html) spacecraft, allowing utilization of co-located cloud mask data provided by the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging) (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane product.\n\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5) mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products) provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone and cloud & aerosol information. These measurements are used for improving air quality forecasts and monitoring the concentrations of atmospheric constituents.\n\nThis STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format, since April 2018 for the following products:\n\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index): Ultraviolet aerosol index\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height): Aerosol layer height\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane): Methane (CH4) total column\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud): Cloud fraction, albedo, and top pressure\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide): Carbon monoxide (CO) total column\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde): Formaldehyde (HCHO) total column\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide): Nitrogen dioxide (NO2) total column\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column): Ozone (O3) total column\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column): Ozone (O3) tropospheric column\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide): Sulfur dioxide (SO2) total column\n* [`L2__NP_BD3`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 3\n* [`L2__NP_BD6`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 6\n* [`L2__NP_BD7`](http://www.tropomi.eu/data-products/auxiliary): Cloud from the Suomi NPP mission, band 7\n", "instrument": "TROPOMI", "keywords": "air-quality,climate-change,copernicus,esa,forecasting,sentinel,sentinel-5-precursor,sentinel-5p,sentinel-5p-l2-netcdf,tropomi", "license": "proprietary", "missionStartDate": "2018-04-30T00:18:50Z", "platform": "Sentinel-5P", "platformSerialIdentifier": "Sentinel 5 Precursor", "processingLevel": null, "title": "Sentinel-5P Level-2"}, "terraclimate": {"abstract": "[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958 to the present. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. This dataset is provided in [Zarr](https://zarr.readthedocs.io/) format.\n", "instrument": null, "keywords": "climate,precipitation,temperature,terraclimate,vapor-pressure,water", "license": "CC0-1.0", "missionStartDate": "1958-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "TerraClimate"}, "us-census": {"abstract": "The [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) counted every person living in the United States and the five U.S. territories. It marked the 24th census in U.S. history and the first time that households were invited to respond to the census online.\n\nThe tables included on the Planetary Computer provide information on population and geographic boundaries at various levels of cartographic aggregation.\n", "instrument": null, "keywords": "administrative-boundaries,demographics,population,us-census,us-census-bureau", "license": "proprietary", "missionStartDate": "2021-08-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "US Census"}, "usda-cdl": {"abstract": "The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission \"to provide timely, accurate and useful statistics in service to U.S. agriculture\" (Johnson and Mueller, 2010, p. 1204). The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season.\n\nThis collection includes Cropland, Confidence, Cultivated, and Frequency products.\n\n- Cropland: Crop-specific land cover data created annually. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.\n- Confidence: The predicted confidence associated with an output pixel. A value of zero indicates low confidence, while a value of 100 indicates high confidence.\n- Cultivated: cultivated and non-cultivated land cover for CONUS based on land cover information derived from the 2017 through 2021 Cropland products.\n- Frequency: crop specific planting frequency based on land cover information derived from the 2008 through 2021 Cropland products.\n\nFor more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).", "instrument": null, "keywords": "agriculture,land-cover,land-use,united-states,usda,usda-cdl", "license": "proprietary", "missionStartDate": "2008-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USDA Cropland Data Layers (CDLs)"}, "usgs-lcmap-conus-v13": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products), which was released in August 2022 for years 1985-2021. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "keywords": "conus,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-conus-v13", "license": "proprietary", "missionStartDate": "1985-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS LCMAP CONUS Collection 1.3"}, "usgs-lcmap-hawaii-v10": {"abstract": "The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \n\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products), which was released in January 2022 for years 2000-2020. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \"nodata\" values where appropriate, and an updated projection definition.\n", "instrument": null, "keywords": "hawaii,land-cover,land-cover-change,lcmap,usgs,usgs-lcmap-hawaii-v10", "license": "proprietary", "missionStartDate": "2000-01-01T00:00:00Z", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "USGS LCMAP Hawaii Collection 1.0"}}, "providers_config": {"3dep-lidar-classification": {"productType": "3dep-lidar-classification"}, "3dep-lidar-copc": {"productType": "3dep-lidar-copc"}, "3dep-lidar-dsm": {"productType": "3dep-lidar-dsm"}, "3dep-lidar-dtm": {"productType": "3dep-lidar-dtm"}, "3dep-lidar-dtm-native": {"productType": "3dep-lidar-dtm-native"}, "3dep-lidar-hag": {"productType": "3dep-lidar-hag"}, "3dep-lidar-intensity": {"productType": "3dep-lidar-intensity"}, "3dep-lidar-pointsourceid": {"productType": "3dep-lidar-pointsourceid"}, "3dep-lidar-returns": {"productType": "3dep-lidar-returns"}, "3dep-seamless": {"productType": "3dep-seamless"}, "alos-dem": {"productType": "alos-dem"}, "alos-fnf-mosaic": {"productType": "alos-fnf-mosaic"}, "alos-palsar-mosaic": {"productType": "alos-palsar-mosaic"}, "aster-l1t": {"productType": "aster-l1t"}, "chesapeake-lc-13": {"productType": "chesapeake-lc-13"}, "chesapeake-lc-7": {"productType": "chesapeake-lc-7"}, "chesapeake-lu": {"productType": "chesapeake-lu"}, "chloris-biomass": {"productType": "chloris-biomass"}, "cil-gdpcir-cc-by": {"productType": "cil-gdpcir-cc-by"}, "cil-gdpcir-cc-by-sa": {"productType": "cil-gdpcir-cc-by-sa"}, "cil-gdpcir-cc0": {"productType": "cil-gdpcir-cc0"}, "conus404": {"productType": "conus404"}, "cop-dem-glo-30": {"productType": "cop-dem-glo-30"}, "cop-dem-glo-90": {"productType": "cop-dem-glo-90"}, "daymet-annual-hi": {"productType": "daymet-annual-hi"}, "daymet-annual-na": {"productType": "daymet-annual-na"}, "daymet-annual-pr": {"productType": "daymet-annual-pr"}, "daymet-daily-hi": {"productType": "daymet-daily-hi"}, "daymet-daily-na": {"productType": "daymet-daily-na"}, "daymet-daily-pr": {"productType": "daymet-daily-pr"}, "daymet-monthly-hi": {"productType": "daymet-monthly-hi"}, "daymet-monthly-na": {"productType": "daymet-monthly-na"}, "daymet-monthly-pr": {"productType": "daymet-monthly-pr"}, "deltares-floods": {"productType": "deltares-floods"}, "deltares-water-availability": {"productType": "deltares-water-availability"}, "drcog-lulc": {"productType": "drcog-lulc"}, "eclipse": {"productType": "eclipse"}, "ecmwf-forecast": {"productType": "ecmwf-forecast"}, "era5-pds": {"productType": "era5-pds"}, "esa-cci-lc": {"productType": "esa-cci-lc"}, "esa-cci-lc-netcdf": {"productType": "esa-cci-lc-netcdf"}, "esa-worldcover": {"productType": "esa-worldcover"}, "fia": {"productType": "fia"}, "fws-nwi": {"productType": "fws-nwi"}, "gap": {"productType": "gap"}, "gbif": {"productType": "gbif"}, "gnatsgo-rasters": {"productType": "gnatsgo-rasters"}, "gnatsgo-tables": {"productType": "gnatsgo-tables"}, "goes-cmi": {"productType": "goes-cmi"}, "goes-glm": {"productType": "goes-glm"}, "gpm-imerg-hhr": {"productType": "gpm-imerg-hhr"}, "gridmet": {"productType": "gridmet"}, "hgb": {"productType": "hgb"}, "hrea": {"productType": "hrea"}, "io-biodiversity": {"productType": "io-biodiversity"}, "io-lulc": {"productType": "io-lulc"}, "io-lulc-9-class": {"productType": "io-lulc-9-class"}, "io-lulc-annual-v02": {"productType": "io-lulc-annual-v02"}, "jrc-gsw": {"productType": "jrc-gsw"}, "kaza-hydroforecast": {"productType": "kaza-hydroforecast"}, "landsat-c2-l1": {"productType": "landsat-c2-l1"}, "landsat-c2-l2": {"productType": "landsat-c2-l2"}, "mobi": {"productType": "mobi"}, "modis-09A1-061": {"productType": "modis-09A1-061"}, "modis-09Q1-061": {"productType": "modis-09Q1-061"}, "modis-10A1-061": {"productType": "modis-10A1-061"}, "modis-10A2-061": {"productType": "modis-10A2-061"}, "modis-11A1-061": {"productType": "modis-11A1-061"}, "modis-11A2-061": {"productType": "modis-11A2-061"}, "modis-13A1-061": {"productType": "modis-13A1-061"}, "modis-13Q1-061": {"productType": "modis-13Q1-061"}, "modis-14A1-061": {"productType": "modis-14A1-061"}, "modis-14A2-061": {"productType": "modis-14A2-061"}, "modis-15A2H-061": {"productType": "modis-15A2H-061"}, "modis-15A3H-061": {"productType": "modis-15A3H-061"}, "modis-16A3GF-061": {"productType": "modis-16A3GF-061"}, "modis-17A2H-061": {"productType": "modis-17A2H-061"}, "modis-17A2HGF-061": {"productType": "modis-17A2HGF-061"}, "modis-17A3HGF-061": {"productType": "modis-17A3HGF-061"}, "modis-21A2-061": {"productType": "modis-21A2-061"}, "modis-43A4-061": {"productType": "modis-43A4-061"}, "modis-64A1-061": {"productType": "modis-64A1-061"}, "ms-buildings": {"productType": "ms-buildings"}, "mtbs": {"productType": "mtbs"}, "naip": {"productType": "naip"}, "nasa-nex-gddp-cmip6": {"productType": "nasa-nex-gddp-cmip6"}, "nasadem": {"productType": "nasadem"}, "noaa-c-cap": {"productType": "noaa-c-cap"}, "noaa-cdr-ocean-heat-content": {"productType": "noaa-cdr-ocean-heat-content"}, "noaa-cdr-ocean-heat-content-netcdf": {"productType": "noaa-cdr-ocean-heat-content-netcdf"}, "noaa-cdr-sea-surface-temperature-optimum-interpolation": {"productType": "noaa-cdr-sea-surface-temperature-optimum-interpolation"}, "noaa-cdr-sea-surface-temperature-whoi": {"productType": "noaa-cdr-sea-surface-temperature-whoi"}, "noaa-cdr-sea-surface-temperature-whoi-netcdf": {"productType": "noaa-cdr-sea-surface-temperature-whoi-netcdf"}, "noaa-climate-normals-gridded": {"productType": "noaa-climate-normals-gridded"}, "noaa-climate-normals-netcdf": {"productType": "noaa-climate-normals-netcdf"}, "noaa-climate-normals-tabular": {"productType": "noaa-climate-normals-tabular"}, "noaa-mrms-qpe-1h-pass1": {"productType": "noaa-mrms-qpe-1h-pass1"}, "noaa-mrms-qpe-1h-pass2": {"productType": "noaa-mrms-qpe-1h-pass2"}, "noaa-mrms-qpe-24h-pass2": {"productType": "noaa-mrms-qpe-24h-pass2"}, "noaa-nclimgrid-monthly": {"productType": "noaa-nclimgrid-monthly"}, "nrcan-landcover": {"productType": "nrcan-landcover"}, "planet-nicfi-analytic": {"productType": "planet-nicfi-analytic"}, "planet-nicfi-visual": {"productType": "planet-nicfi-visual"}, "sentinel-1-grd": {"productType": "sentinel-1-grd"}, "sentinel-1-rtc": {"productType": "sentinel-1-rtc"}, "sentinel-2-l2a": {"productType": "sentinel-2-l2a"}, "sentinel-3-olci-lfr-l2-netcdf": {"productType": "sentinel-3-olci-lfr-l2-netcdf"}, "sentinel-3-olci-wfr-l2-netcdf": {"productType": "sentinel-3-olci-wfr-l2-netcdf"}, "sentinel-3-slstr-frp-l2-netcdf": {"productType": "sentinel-3-slstr-frp-l2-netcdf"}, "sentinel-3-slstr-lst-l2-netcdf": {"productType": "sentinel-3-slstr-lst-l2-netcdf"}, "sentinel-3-slstr-wst-l2-netcdf": {"productType": "sentinel-3-slstr-wst-l2-netcdf"}, "sentinel-3-sral-lan-l2-netcdf": {"productType": "sentinel-3-sral-lan-l2-netcdf"}, "sentinel-3-sral-wat-l2-netcdf": {"productType": "sentinel-3-sral-wat-l2-netcdf"}, "sentinel-3-synergy-aod-l2-netcdf": {"productType": "sentinel-3-synergy-aod-l2-netcdf"}, "sentinel-3-synergy-syn-l2-netcdf": {"productType": "sentinel-3-synergy-syn-l2-netcdf"}, "sentinel-3-synergy-v10-l2-netcdf": {"productType": "sentinel-3-synergy-v10-l2-netcdf"}, "sentinel-3-synergy-vg1-l2-netcdf": {"productType": "sentinel-3-synergy-vg1-l2-netcdf"}, "sentinel-3-synergy-vgp-l2-netcdf": {"productType": "sentinel-3-synergy-vgp-l2-netcdf"}, "sentinel-5p-l2-netcdf": {"productType": "sentinel-5p-l2-netcdf"}, "terraclimate": {"productType": "terraclimate"}, "us-census": {"productType": "us-census"}, "usda-cdl": {"productType": "usda-cdl"}, "usgs-lcmap-conus-v13": {"productType": "usgs-lcmap-conus-v13"}, "usgs-lcmap-hawaii-v10": {"productType": "usgs-lcmap-hawaii-v10"}}}, "usgs_satapi_aws": {"product_types_config": {"landsat-c2ard-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere Brightness Temperature (BT) Product"}, "landsat-c2ard-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Reflectance (SR) Product"}, "landsat-c2ard-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Surface Temperature (ST) Product"}, "landsat-c2ard-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "keywords": "analysis-ready-data,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2ard-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Analysis Ready Data (ARD) Level-2 UTM Top of Atmosphere (TA) Reflectance Product"}, "landsat-c2l1": {"abstract": "The Landsat Level-1 product is a top of atmosphere product distributed as scaled and calibrated digital numbers.", "instrument": null, "keywords": "landsat,landsat-1,landsat-2,landsat-3,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l1", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1972-07-25T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_1,LANDSAT_2,LANDSAT_3,LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-1 Product"}, "landsat-c2l2-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 UTM Surface Reflectance (SR) Product"}, "landsat-c2l2-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 UTM Surface Temperature (ST) Product"}, "landsat-c2l2alb-bt": {"abstract": "The Landsat Top of Atmosphere Brightness Temperature (BT) product is a top of atmosphere product with radiance calculated 'at-sensor', not atmospherically corrected, and expressed in units of Kelvin.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-bt,top-of-atmosphere-brightness-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere Brightness Temperature (BT) Product"}, "landsat-c2l2alb-sr": {"abstract": "The Landsat Surface Reflectance (SR) product measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-sr,surface-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Surface Reflectance (SR) Product"}, "landsat-c2l2alb-st": {"abstract": "The Landsat Surface Temperature (ST) product represents the temperature of the Earth's surface in Kelvin (K).", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-st,surface-temperature", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Surface Temperature (ST) Product"}, "landsat-c2l2alb-ta": {"abstract": "The Landsat Top of Atmosphere (TA) Reflectance product applies per pixel angle band corrections to the Level-1 radiance product.", "instrument": null, "keywords": "landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l2alb-ta,top-of-atmosphere-reflectance", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-2 Albers Top of Atmosphere (TA) Reflectance Product"}, "landsat-c2l3-ba": {"abstract": "The Landsat Burned Area (BA) contains two acquisition-based raster data products that represent burn classification and burn probability.", "instrument": null, "keywords": "analysis-ready-data,burned-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-ba", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-3 Burned Area (BA) Product"}, "landsat-c2l3-dswe": {"abstract": "The Landsat Dynamic Surface Water Extent (DSWE) product contains six acquisition-based raster data products pertaining to the existence and condition of surface water.", "instrument": null, "keywords": "analysis-ready-data,dynamic-surface-water-extent-,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-dswe", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-3 Dynamic Surface Water Extent (DSWE) Product"}, "landsat-c2l3-fsca": {"abstract": "The Landsat Fractional Snow Covered Area (fSCA) product contains an acquisition-based per-pixel snow cover fraction, an acquisition-based revised cloud mask for quality assessment, and a product metadata file.", "instrument": null, "keywords": "analysis-ready-data,fractional-snow-covered-area,landsat,landsat-4,landsat-5,landsat-7,landsat-8,landsat-9,landsat-c2l3-fsca", "license": "https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/Landsat_Data_Policy.pdf", "missionStartDate": "1982-08-22T00:00:00.000Z", "platform": null, "platformSerialIdentifier": "LANDSAT_4,LANDSAT_5,LANDSAT_7,LANDSAT_8,LANDSAT_9", "processingLevel": null, "title": "Landsat Collection 2 Level-3 Fractional Snow Covered Area (fSCA) Product"}}, "providers_config": {"landsat-c2ard-bt": {"productType": "landsat-c2ard-bt"}, "landsat-c2ard-sr": {"productType": "landsat-c2ard-sr"}, "landsat-c2ard-st": {"productType": "landsat-c2ard-st"}, "landsat-c2ard-ta": {"productType": "landsat-c2ard-ta"}, "landsat-c2l1": {"productType": "landsat-c2l1"}, "landsat-c2l2-sr": {"productType": "landsat-c2l2-sr"}, "landsat-c2l2-st": {"productType": "landsat-c2l2-st"}, "landsat-c2l2alb-bt": {"productType": "landsat-c2l2alb-bt"}, "landsat-c2l2alb-sr": {"productType": "landsat-c2l2alb-sr"}, "landsat-c2l2alb-st": {"productType": "landsat-c2l2alb-st"}, "landsat-c2l2alb-ta": {"productType": "landsat-c2l2alb-ta"}, "landsat-c2l3-ba": {"productType": "landsat-c2l3-ba"}, "landsat-c2l3-dswe": {"productType": "landsat-c2l3-dswe"}, "landsat-c2l3-fsca": {"productType": "landsat-c2l3-fsca"}}}, "wekeo_cmems": {"product_types_config": {"EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1D-m_202105": {"abstract": "'''Short description:'''\nThe operational TOPAZ5-ECOSMO Arctic Ocean system uses the ECOSMO biological model coupled online to the TOPAZ5 physical model planned for a future update of the ARCTIC_ANALYSIS_FORECAST_PHYS_002_001_a physical forecast. It is run daily to provide 10 days of forecast of 3D biogeochemical variables ocean. The coupling is done by the FABM framework.\n\nCoupling to a biological ocean model provides a description of the evolution of basic biogeochemical variables. The output consists of daily mean fields interpolated onto a standard grid and 40 fixed levels in NetCDF4 CF format. Variables include 3D fields of nutrients (nitrate, phosphate, silicate), phytoplankton and zooplankton biomass, oxygen, chlorophyll, primary productivity, carbon cycle variables (pH, dissolved inorganic carbon and surface partial CO2 pressure in seawater, carbon export) and light attenuation coefficient. Surface Chlorophyll-a from satellite ocean colour is assimilated every week and projected downwards using the Uitz et al. (2006) method. A new 10-day forecast is produced daily using the previous day's forecast and the most up-to-date prognostic forcing fields.\nOutput products have 6.25 km resolution at the North Pole (equivalent to 1/8 deg) on a stereographic projection. See the Product User Manual for the exact projection parameters.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00003", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-bgc-002-004:cmems-mod-arc-bgc-anfc-ecosmo-p1d-m-202105,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,sea-water-ph-reported-on-total-scale,sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211": {"abstract": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-bgc-002-004:cmems-mod-arc-bgc-anfc-ecosmo-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,sea-water-ph-reported-on-total-scale,sinking-mole-flux-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1D-m_202311": {"abstract": "'''Short description:'''\n\nThe operational TOPAZ5 Arctic Ocean system uses the HYCOM model and a 100-member EnKF assimilation scheme. It is run daily to provide 10 days of forecast (average of 10 members) of the 3D physical ocean, including sea ice with the CICEv5.1 model; data assimilation is performed weekly to provide 7 days of analysis (ensemble average).\n\nOutput products are interpolated on a grid of 6 km resolution at the North Pole on a polar stereographic projection. The geographical projection follows these proj4 library parameters: \n\nproj4 = \"+units=m +proj=stere +lon_0=-45 +lat_0=90 +k=1 +R=6378273 +no_defs\" \n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00001", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-002-001:cmems-mod-arc-phy-anfc-6km-detided-p1d-m-202311,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311": {"abstract": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311", "instrument": null, "keywords": "age-of-first-year-ice,age-of-sea-ice,arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-002-001:cmems-mod-arc-phy-anfc-6km-detided-p1m-m-202311,forecast,fraction-of-first-year-ice,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-x-velocity,sea-water-y-velocity,sst,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011:cmems_mod_arc_phy_anfc_nextsim_P1M-m_202311": {"abstract": "'''Short Description:'''\n\nThe Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, including the Canadian Archipelago, the Baffin and Hudson Bays. neXtSIM is forced with surface atmosphere forcings from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ5, the ARC MFC PHY NRT system (002_001a). neXtSIM runs daily, assimilating manual ice charts, sea ice thickness from CS2SMOS in winter and providing 9-day forecasts. The output variables are the ice concentrations, ice thickness, ice drift velocity, snow depths, sea ice type, sea ice age, ridge volume fraction and albedo, provided at hourly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.\n\n\n'''DOI (product) :''' \n\nhttps://doi.org/10.48670/moi-00004", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-analysisforecast-phy-ice-002-011:cmems-mod-arc-phy-anfc-nextsim-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-ice-age,sea-ice-albedo,sea-ice-area-fraction,sea-ice-classification,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-volume-fraction-of-ridged-ice,sea-ice-x-velocity,sea-ice-y-velocity,surface-snow-thickness,target-application#seaiceservices,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-11-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Analysis and Forecast"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1D-m_202105": {"abstract": "'''Short description:'''\n\nThe TOPAZ-ECOSMO reanalysis system assimilates satellite chlorophyll observations and in situ nutrient profiles. The model uses the Hybrid Coordinate Ocean Model (HYCOM) coupled online to a sea ice model and the ECOSMO biogeochemical model. It uses the Determinstic version of the Ensemble Kalman Smoother to assimilate remotely sensed colour data and nutrient profiles. Data assimilation, including the 80-member ensemble production, is performed every 8-days. Atmospheric forcing fields from the ECMWF ERA-5 dataset are used.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00006", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-bgc-002-005:cmems-mod-arc-bgc-my-ecosmo-p1d-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-bgc-002-005:cmems-mod-arc-bgc-my-ecosmo-p1m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-bgc-002-005:cmems-mod-arc-bgc-my-ecosmo-p1y-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-sea-level,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-hflux-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-hflux-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-mflux-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-mflux-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-topaz4-p1d-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1M_202012": {"abstract": "'''Short description:'''\n\nThe current version of the TOPAZ system - TOPAZ4b - is nearly identical to the real-time forecast system run at MET Norway. It uses a recent version of the Hybrid Coordinate Ocean Model (HYCOM) developed at University of Miami (Bleck 2002). HYCOM is coupled to a sea ice model; ice thermodynamics are described in Drange and Simonsen (1996) and the elastic-viscous-plastic rheology in Hunke and Dukowicz (1997). The model's native grid covers the Arctic and North Atlantic Oceans, has fairly homogeneous horizontal spacing (between 11 and 16 km). 50 hybrid layers are used in the vertical (z-isopycnal), more than the TOPAZ4 system (28 layers). TOPAZ4b uses the Deterministic version of the Ensemble Kalman filter (DEnKF; Sakov and Oke 2008) to assimilate remotely sensed as well as temperature and salinity profiles. The output is interpolated onto standard grids and depths. Daily values are provided at all depths. Data assimilation, including the 100-member ensemble production, is performed weekly.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00007", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-topaz4-p1m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:arctic-multiyear-phy-002-003:cmems-mod-arc-phy-my-topaz4-p1y-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,numerical-model,ocean-barotropic-streamfunction,ocean-mixed-layer-thickness,oceanographic-geographical-features,sea-floor-depth-below-sea-level,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-ice-x-velocity,sea-ice-y-velocity,sea-level,sea-surface-elevation,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,surface-snow-thickness,weather-climate-and-seasonal-forecasting,x-sea-water-velocity,y-sea-water-velocity", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1991-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Physics Reanalysis"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411", "instrument": null, "keywords": "eo:mo:dat:arctic-multiyear-phy-ice-002-016:cmems-mod-arc-phy-my-nextsim-p1d-m-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411": {"abstract": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411", "instrument": null, "keywords": "eo:mo:dat:arctic-multiyear-phy-ice-002-016:cmems-mod-arc-phy-my-nextsim-p1m-m-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_7-10days_P1D-i_202411": {"abstract": "'''Short description:'''\n\nThis Baltic Sea biogeochemical model product provides forecasts for the biogeochemical conditions in the Baltic Sea. The Baltic forecast is updated daily providing a new six days forecast. Three different datasets are provided. One with daily means and one with monthly means values for these parameters: nitrate, phosphate, chl-a, ammonium, dissolved oxygen, ph, phytoplankton, zooplankton, silicate, dissolved inorganic carbon, and partial pressure of co2 at the surface. Instantaenous values for the Secchi Depth and light attenuation valid for noon (12Z) are included in the daily mean files/dataset. Additionally a third dataset with daily accumulated values of the netto primary production is available. The product is produced by the biogeochemical model ERGOM (Neumann, 2000) one way coupled to a Baltic Sea set up of the NEMO ocean model, which provides the CMEMS Baltic physical ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). This biogeochemical product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and up to 56 vertical depth levels. The product covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00009", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-pp-anfc-7-10days-p1d-i-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-pp-anfc-p1d-i-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-anfc-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-anfc-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-bgc-003-007:cmems-mod-bal-bgc-anfc-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-cur-anfc-detided-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-cur-anfc-detided-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-ssh-anfc-detided-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThis Baltic Sea physical model product provides forecasts for the physical conditions in the Baltic Sea. The Baltic forecast is updated twice daily providing a new six days forecast. Several datasets are provided: One with hourly instantaneous values, one with daily mean values and one with monthly mean values, all containing these parameters: sea level variations, ice concentration and thickness at the surface, and temperature, salinity and horizontal and vertical velocities for the 3D field. Additionally a dataset with 15 minutes (instantaneous) surface values are provided for the sea level variation and the surface horizontal currents. The product is produced by a Baltic Sea set up of the NEMOv4.0 ocean model. This product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and up to 56 vertical depth levels. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The ocean model is forced with Stokes drift data from the Baltic Wave forecast product (BALTICSEA_ANALYSISFORECAST_WAV_003_010). Satellite SST, ice concentrations and in-situ T and S profiles are assimilated into the model's analysis field. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00010", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-ssh-anfc-detided-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": [], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-7-10days-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-7-10days-pt15m-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-7-10days-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1M-m_202311": {"abstract": "'''Short description:'''\n\nThis Baltic Sea physical model product provides forecasts for the physical conditions in the Baltic Sea. The Baltic forecast is updated twice daily providing a new six days forecast. Several datasets are provided: One with hourly instantaneous values, one with daily mean values and one with monthly mean values, all containing these parameters: sea level variations, ice concentration and thickness at the surface, and temperature, salinity and horizontal and vertical velocities for the 3D field. Additionally a dataset with 15 minutes (instantaneous) surface values are provided for the sea level variation and the surface horizontal currents. The product is produced by a Baltic Sea set up of the NEMOv4.0 ocean model. This product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and up to 56 vertical depth levels. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The ocean model is forced with Stokes drift data from the Baltic Wave forecast product (BALTICSEA_ANALYSISFORECAST_WAV_003_010). Satellite SST, ice concentrations and in-situ T and S profiles are assimilated into the model's analysis field. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00010", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-pt15m-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-assuming-no-tide,eo:mo:dat:balticsea-analysisforecast-phy-003-006:cmems-mod-bal-phy-anfc-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tide,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,s,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid-assuming-no-tide,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,t,target-application#seaiceservices,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-wav-003-010:cmems-mod-bal-wav-anfc-7-10days-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_PT1H-i_202311": {"abstract": "'''Short description:'''\n\nThis Baltic Sea wave model product provides forecasts for the wave conditions in the Baltic Sea. The Baltic forecast is updated twice a day providing a new six days forecast with hourly instantaneous data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the Stokes drift, and two paramters for the maximum wave. The product is based on the wave model WAM cycle 4.7. The wave model is forced with surface currents, sea level anomaly and ice information from the CMEMS BAL MFC ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00011", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:balticsea-analysisforecast-wav-003-010:cmems-mod-bal-wav-anfc-pt1h-i-202311,forecast,level-4,marine-resources,marine-safety,near-real-time,none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Wave Analysis and Forecast"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-bgc-003-012:cmems-mod-bal-bgc-my-p1d-m-202303,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-bgc-003-012:cmems-mod-bal-bgc-my-p1m-m-202303,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1Y-m_202303": {"abstract": "'''Short description:'''\n\nThis Baltic Sea Biogeochemical Reanalysis product provides a biogeochemical reanalysis for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the biogeochemical model ERGOM one-way online-coupled with the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include nitrate, phosphate, ammonium, dissolved oxygen, ph, chlorophyll-a, secchi depth, surface partial co2 pressure and net primary production. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n'''DOI (product) :'''\n\nhttps://doi.org/10.48670/moi-00012", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:balticsea-multiyear-bgc-003-012:cmems-mod-bal-bgc-my-p1y-m-202303,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water(at-bottom),mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water(daily-accumulated),none,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,secchi-depth-of-sea-water,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1D-m_202303": {"abstract": "'''Short description:'''\n\nThis Baltic Sea Physical Reanalysis product provides a reanalysis for the physical conditions for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include sea level, ice concentration, ice thickness, salinity, temperature, horizonal velocities and the mixed layer depths. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00013", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:balticsea-multiyear-phy-003-011:cmems-mod-bal-phy-my-p1d-m-202303,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:balticsea-multiyear-phy-003-011:cmems-mod-bal-phy-my-p1m-m-202303,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303", "instrument": null, "keywords": "baltic-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:balticsea-multiyear-phy-003-011:cmems-mod-bal-phy-my-p1y-m-202303,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-thickness,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sea-water-salinity(at-bottom),sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Physics Reanalysis"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411", "instrument": null, "keywords": "eo:mo:dat:balticsea-multiyear-wav-003-015:cmems-mod-bal-wav-my-2km-climatology-p1m-m-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411", "instrument": null, "keywords": "eo:mo:dat:balticsea-multiyear-wav-003-015:cmems-mod-bal-wav-my-pt1h-i-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411": {"abstract": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411", "instrument": null, "keywords": "eo:mo:dat:balticsea-multiyear-wav-003-015:cmems-mod-bal-wav-my-aflux-pt1h-i-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-bio-anfc-3km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-bio-anfc-3km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-car-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-car-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-car-anfc-3km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-car-anfc-3km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-2.5km-pt1h-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-3km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-3km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_PT1H-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_PT1H-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-co2-anfc-3km-pt1h-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-nut-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-nut-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-nut-anfc-3km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-nut-anfc-3km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1D-m_202311": {"abstract": "'''Short description:''' \n\nBLKSEA_ANALYSISFORECAST_BGC_007_010 is the nominal product of the Black Sea Biogeochemistry NRT system and is generated by the NEMO 4.0-BAMHBI modelling system. Biogeochemical Model for Hypoxic and Benthic Influenced areas (BAMHBI) is an innovative biogeochemical model with a 28-variable pelagic component (including the carbonate system) and a 6-variable benthic component ; it explicitely represents processes in the anoxic layer.\nThe product provides analysis and forecast for 3D concentration of chlorophyll, nutrients (nitrate and phosphate), dissolved oxygen, phytoplankton carbon biomass, net primary production, pH, dissolved inorganic carbon, total alkalinity, and for 2D fields of bottom oxygen concentration (for the North-Western shelf), surface partial pressure of CO2 and surface flux of CO2. These variables are computed on a grid with ~3km x 59-levels resolution, and are provided as daily and monthly means.\n\n'''Product Citation:''' \n\nPlease refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (product) :''' \nhttps://doi.org/10.25423/cmcc/blksea_analysisforecast_bgc_007_010", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-opt-anfc-3km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-opt-anfc-3km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-optics-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-optics-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pft-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pft-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pft-anfc-3km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pft-anfc-3km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pp-o2-anfc-2.5km-p1d-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,downwelling-photosynthetic-photon-flux-in-sea-water,eo:mo:dat:blksea-analysisforecast-bgc-007-010:cmems-mod-blk-bgc-pp-o2-anfc-2.5km-p1m-m-202411,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-detided-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-detided-2.5km-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i_202311": {"abstract": "'''Short description''': \n\nThe BLKSEA_ANALYSISFORECAST_PHY_007_001 is produced with a hydrodynamic model implemented over the whole Black Sea basin, including the Bosporus Strait and a portion of the Marmara Sea for the optimal interface with the Mediterranean Sea through lateral open boundary conditions. The model horizontal grid resolution is 1/40\u00b0 in zonal and 1/40\u00b0 in meridional direction (ca. 121 km) and has 121 unevenly spaced vertical levels. The product provides analysis and forecast for 3D potential temperature, salinity, horizontal and vertical currents. Together with the 2D variables sea surface height, bottom potential temperature and mixed layer thickness.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/cmcc/blksea_analysisforecast_phy_007_001_eas6", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-cur-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-mld-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-mld-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-mld-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-sal-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-detided-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-detided-2.5km-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-ssh-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-2.5km-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-2.5km-p1m-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_PT1H-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_PT1H-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-2.5km-pt1h-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-mrm-500m-p1d-m-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-tem-anfc-mrm-500m-pt1h-i-202311,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-temp-anfc-2.5km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-temp-anfc-2.5km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-detided,eo:mo:dat:blksea-analysisforecast-phy-007-001:cmems-mod-blk-phy-temp-anfc-2.5km-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-detided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-detided,sea-surface-height-above-sea-level,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_WAV_007_003:cmems_mod_blk_wav_anfc_2.5km_PT1H-i_202411": {"abstract": "'''Short description''': \n\nThe wave analysis and forecasts for the Black Sea are produced with the third generation spectral wave model WAM Cycle 6. The hindcast and ten days forecast are produced twice a day on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74518. The model takes into account depth refraction, wave breaking, and assimilation of satellite wave and wind data. The system provides a hindcast and ten days forecast with one-hourly output twice a day. The atmospheric forcing is taken from ECMWF analyses and forecast data. Additionally, WAM is forced by surface currents and sea surface height from BLKSEA_ANALYSISFORECAST_PHY_007_001. Monthly statistics are provided operationally on the Product Quality Dashboard following the CMEMS metrics definitions.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:blksea-analysisforecast-wav-007-003:cmems-mod-blk-wav-anfc-2.5km-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-maximum-period,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Analysis and Forecast"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-bio-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-car-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-co2-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1D-m_202311": {"abstract": "'''Short description:''' \n\nThe biogeochemical reanalysis for the Black Sea is produced by the MAST/ULiege Production Unit by means of the BAMHBI biogeochemical model. The workflow runs on the CECI hpc infrastructure (Wallonia, Belgium).\n\n''Product Citation'': Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n''DOI (product)'': https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_BGC_007_005_BAMHBI", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-nut-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-p1d-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-p1y-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-my-2.5km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eo:mo:dat:blksea-multiyear-bgc-007-005:cmems-mod-blk-bgc-plankton-myint-2.5km-p1m-m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-height-above-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m_202411": {"abstract": "'''Short description''': \n\nThe BLKSEA_MULTIYEAR_PHY_007_004 product provides monthly and daily ocean fields for the Black Sea basin starting from 01/01/1993. The hydrodynamical core is based on NEMO general circulation ocean model, implemented in the BS domain with horizontal resolution of 1/27\u00b0 x 1/36\u00b0 and 31 vertical levels. NEMO is forced by atmospheric surface fluxes computed by bulk formulation using ECMWF ERA5 atmospheric fields at the resolution of 0.25\u00b0 in space and 1-h in time. The current version has closed boundary at the Bosporus Strait. The model is online coupled to OceanVar assimilation scheme to assimilate sea level anomaly along-track observations from CMEMS and available in situ vertical profiles of temperature and salinity from both SeaDataNet and CMEMS datasets. \n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-cur-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-hflux-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-hflux-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mflux-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mflux-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-mld-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-sal-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-ssh-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-climatology-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-my-2.5km-p1y-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-temp-myint-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-wflux-my-2.5km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411", "instrument": null, "keywords": "black-sea,cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:blksea-multiyear-phy-007-004:cmems-mod-blk-phy-wflux-my-2.5km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,multi-year,net-downward-shortwave-flux-at-sea-water-surface,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,precipitation-flux,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,surface-downward-latent-heat-flux,surface-downward-sensible-heat-flux,surface-downward-x-stress,surface-downward-y-stress,surface-net-downward-longwave-flux,water-evaporation-flux,water-flux-into-sea-water-from-rivers,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Physics Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-aflux-my-2.5km-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m_202311": {"abstract": "'''Short description''': \nThe wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40\u00b0 x 1/40\u00b0) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74,518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/cmcc/blksea_multiyear_wav_007_006_eas4", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-my-2.5km-climatology-pt1m-m-202311,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-my-2.5km-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411": {"abstract": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:blksea-multiyear-wav-007-006:cmems-mod-blk-wav-myint-2.5km-pt1h-i-202411,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1950-01-08", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea Waves Reanalysis"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-bio-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-bio-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-car-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-car-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-co2-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-co2-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-nut-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-nut-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-optics-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m_202311": {"abstract": "'''Short description:'''\n\nThe Operational Mercator Ocean biogeochemical global ocean analysis and forecast system at 1/4 degree is providing 10 days of 3D global ocean forecasts updated weekly. The time series is aggregated in time, in order to reach a two full year\u2019s time series sliding window. This product includes daily and monthly mean files of biogeochemical parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production, phytoplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5700 meters.\n\n* NEMO version (v3.6_STABLE)\n* Forcings: GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 at daily frequency. \n* Outputs mean fields are interpolated on a standard regular grid in NetCDF format.\n* Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID[http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-028.pdf] \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00015", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-optics-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-pft-anfc-0.25deg-p1d-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-pft-anfc-0.25deg-p1m-m-202311,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-plankton-anfc-0.25deg-p1d-m-202411,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411", "instrument": null, "keywords": "brest,cell-height,cell-thickness,cell-width,coastal-marine-environment,eo:mo:dat:global-analysisforecast-bgc-001-028:cmems-mod-glo-bgc-plankton-anfc-0.25deg-p1m-m-202411,forecast,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,model-level-number-at-sea-floor,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-cur-anfc-0.083deg-p1d-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m_202406": {"abstract": "'''Short description'''\n\nThe Operational Mercator global ocean analysis and forecast system at 1/12 degree is providing 10 days of 3D global ocean forecasts updated daily. The time series is aggregated in time in order to reach a two full year\u2019s time series sliding window.\n\nThis product includes daily and monthly mean files of temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom over the global ocean. It also includes hourly mean surface fields for sea level height, temperature and currents. The global ocean output files are displayed with a 1/12 degree horizontal resolution with regular longitude/latitude equirectangular projection.\n\n50 vertical levels are ranging from 0 to 5500 meters.\n\nThis product also delivers a special dataset for surface current which also includes wave and tidal drift called SMOC (Surface merged Ocean Current).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00016", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-cur-anfc-0.083deg-p1m-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-cur-anfc-0.083deg-pt6h-i-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-so-anfc-0.083deg-p1d-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-so-anfc-0.083deg-p1m-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-so-anfc-0.083deg-pt6h-i-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-thetao-anfc-0.083deg-p1d-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-thetao-anfc-0.083deg-p1m-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-thetao-anfc-0.083deg-pt6h-i-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-wcur-anfc-0.083deg-p1d-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-wcur-anfc-0.083deg-p1m-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-climatology-uncertainty-p1m-m-202311,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-sst-anomaly-p1d-m-202411,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-sst-anomaly-p1m-m-202411,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-p1d-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-p1m-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-0.083deg-pt1h-m-202406,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-merged-sl-pt1h-i-202411,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211": {"abstract": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211", "instrument": null, "keywords": "age-of-sea-ice,cell-thickness,coastal-marine-environment,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-analysisforecast-phy-001-024:cmems-mod-glo-phy-anfc-merged-uv-pt1h-i-202211,forecast,global-ocean,in-situ-ts-profiles,invariant,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,near-real-time,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-albedo,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-speed,sea-ice-surface-temperature,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-water-potential-salinity-at-sea-floor,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-pressure-at-sea-floor,sea-water-salinity,sst,surface-sea-water-x-velocity,surface-sea-water-x-velocity-due-to-tide,surface-sea-water-y-velocity,surface-sea-water-y-velocity-due-to-tide,surface-snow-thickness,target-application#seaiceforecastingapplication,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-06-18", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_WAV_001_027:cmems_mod_glo_wav_anfc_0.083deg_PT3H-i_202411": {"abstract": "'''Short description:'''\n \nThe operational global ocean analysis and forecast system of M\u00e9t\u00e9o-France with a resolution of 1/12 degree is providing daily analyses and 10 days forecasts for the global ocean sea surface waves. This product includes 3-hourly instantaneous fields of integrated wave parameters from the total spectrum (significant height, period, direction, Stokes drift,...etc), as well as the following partitions: the wind wave, the primary and secondary swell waves.\n \nThe global wave system of M\u00e9t\u00e9o-France is based on the wave model MFWAM which is a third generation wave model. MFWAM uses the computing code ECWAM-IFS-38R2 with a dissipation terms developed by Ardhuin et al. (2010). The model MFWAM was upgraded on november 2014 thanks to improvements obtained from the european research project \u00ab my wave \u00bb (Janssen et al. 2014). The model mean bathymetry is generated by using 2-minute gridded global topography data ETOPO2/NOAA. Native model grid is irregular with decreasing distance in the latitudinal direction close to the poles. At the equator the distance in the latitudinal direction is more or less fixed with grid size 1/10\u00b0. The operational model MFWAM is driven by 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF atmospheric system. The wave spectrum is discretized in 24 directions and 30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM uses the assimilation of altimeters with a time step of 6 hours. The global wave system provides analysis 4 times a day, and a forecast of 10 days at 0:00 UTC. The wave model MFWAM uses the partitioning to split the swell spectrum in primary and secondary swells.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00017", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-analysisforecast-wav-001-027:cmems-mod-glo-wav-anfc-0.083deg-pt3h-i-202411,forecast,global-ocean,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Analysis and Forecast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-my-0.25deg-p1d-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-my-0.25deg-p1m-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1D-m_202406": {"abstract": "'''Short description'''\n\nThe biogeochemical hindcast for global ocean is produced at Mercator-Ocean (Toulouse. France). It provides 3D biogeochemical fields since year 1993 at 1/4 degree and on 75 vertical levels. It uses PISCES biogeochemical model (available on the NEMO[https://www.nemo-ocean.eu/] modelling platform). No data assimilation in this product.\n\n* Latest NEMO version (v3.6_STABLE)\n* Forcings: FREEGLORYS2V4[https://www.mercator-ocean.fr/en/solutions-expertise/how-to-access-the-mercator-ocean-services/let-s-define-your-needs/] ocean physics produced at Mercator-Ocean and ERA-Interim[https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim] atmosphere produced at ECMWF at a daily frequency \n* Outputs: Daily (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production) and monthly (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production. iron. phytoplankton in carbon) 3D mean fields interpolated on a standard regular grid in NetCDF format. The simulation is performed once and for all.\n* Initial conditions: World Ocean Atlas 2013 for nitrate. phosphate. silicate and dissolved oxygen. GLODAPv2 for DIC and Alkalinity. and climatological model outputs for Iron and DOC \n* Quality/Accuracy/Calibration information: See the related QuID[http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-029.pdf]\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00019", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-myint-0.25deg-p1d-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,brest,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:global-multiyear-bgc-001-029:cmems-mod-glo-bgc-myint-0.25deg-p1m-m-202406,global-ocean,invariant,level-4,marine-resources,marine-safety,modelling-data,multi-year,none,north-mid-atlantic-ridge,numerical-model,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "2023-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Biogeochemistry Hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i_202411": {"abstract": "'''Short description:'''\nThe Low and Mid-Trophic Levels (LMTL) reanalysis for global ocean is produced at [https://www.cls.fr CLS] on behalf of Global Ocean Marine Forecasting Center. It provides 2D fields of biomass content of zooplankton and six functional groups of micronekton. It uses the LMTL component of SEAPODYM dynamical population model (http://www.seapodym.eu). No data assimilation has been done. This product also contains forcing data: net primary production, euphotic depth, depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers.\n\n'''Forcings sources:'''\n* Ocean currents and temperature (CMEMS multiyear product)\n* Net Primary Production computed from chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high latitudes (CMEMS biogeochemistry multiyear product)\n\n'''Vertical coverage:'''\n* Epipelagic layer \n* Upper mesopelagic layer\n* Lower mesopelagic layer (max. 1000m)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00020", "instrument": null, "keywords": "/biological-oceanography/phytoplankton-and-microphytobenthos,/biological-oceanography/zooplankton,atlantic-ocean,coastal-marine-environment,data,eastward-sea-water-velocity-vertical-mean-over-pelagic-layer,eo:mo:dat:global-multiyear-bgc-001-033:cmems-mod-glo-bgc-my-0.083deg-lmtl-fphy-pt1d-i-202411,euphotic-zone-depth,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-zooplankton-expressed-as-carbon-in-sea-water,modelling-data,multi-year,net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water,north-mid-atlantic-ridge,northward-sea-water-velocity-vertical-mean-over-pelagic-layer,not-applicable,numerical-model,oceanographic-geographical-features,sea-water-pelagic-layer-bottom-depth,sea-water-potential-temperature-vertical-mean-over-pelagic-layer,weather-climate-and-seasonal-forecasting,wp3-pelagic-mapping", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411", "instrument": null, "keywords": "/biological-oceanography/phytoplankton-and-microphytobenthos,/biological-oceanography/zooplankton,atlantic-ocean,coastal-marine-environment,data,eastward-sea-water-velocity-vertical-mean-over-pelagic-layer,eo:mo:dat:global-multiyear-bgc-001-033:cmems-mod-glo-bgc-my-0.083deg-lmtl-pt1d-i-202411,euphotic-zone-depth,global-ocean,invariant,level-4,marine-resources,marine-safety,mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water,mass-content-of-zooplankton-expressed-as-carbon-in-sea-water,modelling-data,multi-year,net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water,north-mid-atlantic-ridge,northward-sea-water-velocity-vertical-mean-over-pelagic-layer,not-applicable,numerical-model,oceanographic-geographical-features,sea-water-pelagic-layer-bottom-depth,sea-water-potential-temperature-vertical-mean-over-pelagic-layer,weather-climate-and-seasonal-forecasting,wp3-pelagic-mapping", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global ocean low and mid trophic levels biomass content hindcast"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-my-0.083deg-climatology-p1m-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-my-0.083deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1M-m_202311": {"abstract": "'''Short description:'''\n \nThe GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12\u00b0 horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward).\n\nIt is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.\n\nThis product includes daily and monthly mean files for temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom. The global ocean output files are displayed on a standard regular grid at 1/12\u00b0 (approximatively 8 km) and on 50 standard levels.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00021", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-my-0.083deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-myint-0.083deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,cell-thickness,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-ice-velocity,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-001-030:cmems-mod-glo-phy-myint-0.083deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,invariant,kuala-lumpur,level-4,marine-resources,marine-safety,model-level-number-at-sea-floor,modelling-data,multi-year,north-mid-atlantic-ridge,northward-sea-ice-velocity,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-ice-area-fraction,sea-ice-concentration-and/or-thickness,sea-ice-thickness,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-all-my-0.25deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-all-my-0.25deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-mnstd-my-0.25deg-p1d-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m_202311": {"abstract": "'''Short description:'''\n\n You can find here the CMEMS Global Ocean Ensemble Reanalysis product at \u00bc degree resolution : monthly means of Temperature, Salinity, Currents and Ice variables for 75 vertical levels, starting from 1993 onward.\n \nGlobal ocean reanalyses are homogeneous 3D gridded descriptions of the physical state of the ocean covering several decades, produced with a numerical ocean model constrained with data assimilation of satellite and in situ observations. These reanalyses are built to be as close as possible to the observations (i.e. realistic) and in agreement with the model physics The multi-model ensemble approach allows uncertainties or error bars in the ocean state to be estimated.\n\nThe ensemble mean may even provide for certain regions and/or periods a more reliable estimate than any individual reanalysis product.\n\nThe four reanalyses, used to create the ensemble, covering \u201caltimetric era\u201d period (starting from 1st of January 1993) during which altimeter altimetry data observations are available:\n * GLORYS2V4 from Mercator Ocean (Fr);\n * ORAS5 from ECMWF;\n * GloSea5 from Met Office (UK);\n * and C-GLORSv7 from CMCC (It);\n \nThese four products provided four different time series of global ocean simulations 3D monthly estimates. All numerical products available for users are monthly or daily mean averages describing the ocean.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00024", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:global-multiyear-phy-ens-001-031:cmems-mod-glo-phy-mnstd-my-0.25deg-p1m-m-202311,global-ocean,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-ice-concentration-and/or-thickness,sea-ice-fraction,sea-ice-thickness,sea-level,sea-surface-height,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-15", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Ensemble Physics Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-multiyear-wav-001-032:cmems-mod-glo-wav-my-0.2deg-climatology-p1m-m-202311,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411": {"abstract": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-multiyear-wav-001-032:cmems-mod-glo-wav-my-0.2deg-pt3h-i-202411,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Reanalysis"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_myint_0.2deg_PT3H-i_202311": {"abstract": "'''Short description:'''\n\nGLOBAL_REANALYSIS_WAV_001_032 for the global wave reanalysis describing past sea states since years 1993. This product also bears the name of WAVERYS within the GLO-HR MFC. for correspondence to other global multi-year products like GLORYS. BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5\u00b0 irregular grid. Average wave quantities derived from this wave spectrum. such as the SWH (significant wave height) or the average wave period. are delivered on a regular 1/5\u00b0 grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from a geometric sequence of first member 0.035 Hz and a reason 7.5. WAVERYS takes into account oceanic currents from the GLORYS12 physical ocean reanalysis and assimilates significant wave height observed from historical altimetry missions and directional wave spectra from Sentinel 1 SAR from 2017 onwards. \n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00022", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:global-multiyear-wav-001-032:cmems-mod-glo-wav-myint-0.2deg-pt3h-i-202311,global-ocean,invariant,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2023-04-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Waves Reanalysis"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1D-m_202411": {"abstract": "'''Short description:'''\nThe IBI-MFC provides a high-resolution biogeochemical analysis and forecast product covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as daily averaged forecasts with a horizon of 10 days (updated on a weekly basis) are available on the catalogue.\nTo this aim, an online coupled physical-biogeochemical operational system is based on NEMO-PISCES at 1/36\u00b0 and adapted to the IBI area, being Mercator-Ocean in charge of the model code development. PISCES is a model of intermediate complexity, with 24 prognostic variables. It simulates marine biological productivity of the lower trophic levels and describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe).\nThe product provides daily and monthly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, surface partial pressure of carbon dioxide, and zooplankton.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00026", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-optics-anfc-0.027deg-p1d-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-optics-anfc-0.027deg-p1m-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-anfc-0.027deg-3d-p1d-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:ibi-analysisforecast-bgc-005-004:cmems-mod-ibi-bgc-anfc-0.027deg-3d-p1m-m-202411,euphotic-zone-depth,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "\"''Short description:'''\nThe IBI-MFC provides a high-resolution ocean analysis and forecast product (daily run by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as forecasts of different temporal resolutions with a horizon of 5 days (updated on a daily basis) are available on the catalogue.\nThe system is based on a eddy-resolving NEMO model application at 1/36\u00ba horizontal resolution, being Mercator-Ocean in charge of the model code development. The hydrodynamic forecast includes high frequency processes of paramount importance to characterize regional scale marine processes: tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge, wave forcing in forecast, etc. A weekly update of IBI downscaled analysis is also delivered as historic IBI best estimates.\nThe product offers 3D daily and monthly ocean fields, as well as hourly mean and 15-minute instantaneous values for some surface variables. Daily and monthly averages of 3D Temperature, 3D Salinity, 3D Zonal and Meridional Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are delivered. Finally, 15-minute instantaneous values of Sea Surface Height and Currents are also given.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00027", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-cur-anfc-detided-0.027deg-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-cur-anfc-detided-0.027deg-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-ssh-anfc-detided-0.027deg-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-ssh-anfc-detided-0.027deg-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-wcur-anfc-0.027deg-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-wcur-anfc-0.027deg-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-2d-pt15m-i-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-2d-pt1h-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-3d-p1d-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-3d-p1m-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411", "instrument": null, "keywords": "barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:ibi-analysisforecast-phy-005-001:cmems-mod-ibi-phy-anfc-0.027deg-3d-pt1h-m-202411,forecast,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-analysisforecast-wav-005-005:cmems-mod-ibi-wav-anfc-0.027deg-pt1h-i-202411,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),wave-spectra,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.05deg_PT1H-i_202311": {"abstract": "'''Short description:'''\nThe IBI-MFC provides a high-resolution wave analysis and forecast product (run twice a day by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia\u2013Biscay\u2013Ireland (IBI) area. The last 2 years before now (historic best estimates), as well as hourly instantaneous forecasts with a horizon of up to 10 days (updated on a daily basis) are available on the catalogue.\nThe IBI wave model system is based on the MFWAM model and runs on a grid of 5 km of horizontal resolution forced with the ECMWF hourly wind data. The system assimilates significant wave height (SWH) altimeter data and CFOSAT wave spectral data (supplied by M\u00e9t\u00e9o-France), and it is forced by currents provided by the IBI ocean circulation system. \nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Additionally, the IBI wave system is set up to provide internally some key parameters adequate to be used as forcing in the IBI NEMO ocean model forecast run.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00025", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-analysisforecast-wav-005-005:cmems-mod-ibi-wav-anfc-0.05deg-pt1h-i-202311,forecast,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-spectral-peak,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),wave-spectra,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-my-0.083deg-p1d-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-my-0.083deg-p1m-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-plankton-my-0.083deg-p1y-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-climatology-p1m-m-202411,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-p1d-m-202012,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-p1m-m-202012,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m_202211": {"abstract": "'''Short description:'''\nThe IBI-MFC provides a biogeochemical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources.\nTo this aim, an application of the biogeochemical model PISCES is run simultaneously with the ocean physical IBI reanalysis, generating both products at the same 1/12\u00b0 horizontal resolution. The PISCES model is able to simulate the first levels of the marine food web, from nutrients up to mesozooplankton and it has 24 state variables.\nThe product provides daily, monthly and yearly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon and surface partial pressure of carbon dioxide. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00028", "instrument": null, "keywords": "/biological-oceanography/other-biological-measurements,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:ibi-multiyear-bgc-005-003:cmems-mod-ibi-bgc-my-0.083deg-3d-p1y-m-202211,euphotic-zone-depth,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,modelling-data,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-11-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-hflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-hflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-mflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-mflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wcur-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wcur-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wcur-0.083deg-p1y-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wflux-0.083deg-p1d-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-wflux-0.083deg-p1m-m-202411,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-2d-pt1h-m-202012,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-climatology-p1m-m-202211,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-p1d-m-202012,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-p1m-m-202012,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m_202211": {"abstract": "'''Short description:'''\nThe IBI-MFC provides a ocean physical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user\u2019s format by Nologin with the support of CESGA in terms of supercomputing resources. \nThe IBI model numerical core is based on the NEMO v3.6 ocean general circulation model run at 1/12\u00b0 horizontal resolution. Altimeter data, in situ temperature and salinity vertical profiles and satellite sea surface temperature are assimilated.\nThe product offers 3D daily, monthly and yearly ocean fields, as well as hourly mean fields for surface variables. Daily, monthly and yearly averages of 3D Temperature, 3D Salinity, 3D Zonal and Meridional Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are distributed. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00029", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,barotropic-eastward-sea-water-velocity,barotropic-northward-sea-water-velocity,celtic-seas,coastal-marine-environment,data,drivers-and-tipping-points,eastward-sea-water-velocity,eo:mo:dat:ibi-multiyear-phy-005-002:cmems-mod-ibi-phy-my-0.083deg-3d-p1y-m-202211,iberian-biscay-irish-seas,in-situ-ts-profiles,level-4,marine-resources,marine-safety,modelling-data,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-multiyear-wav-005-006:cmems-mod-ibi-wav-my-aflux-0.027deg-p1h-i-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m_202311": {"abstract": "'''Short description:'''\nThe IBI-MFC provides a high-resolution wave reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly extended on a yearly basis. The model system is run by Nologin with the support of CESGA in terms of supercomputing resources. \nThe Multi-Year model configuration is based on the MFWAM model developed by M\u00e9t\u00e9o-France (MF), covering the same region as the IBI-MFC Near Real Time (NRT) analysis and forecasting product, but with an enhanced horizontal resolution (1/36\u00ba instead of 1/20\u00ba). The system assimilates significant wave height (SWH) altimeter data and wave spectral data (Envisat and CFOSAT), supplied by MF. Both, the MY and the NRT products, are fed by ECMWF hourly winds. Specifically, the MY system is forced by the ERA5 reanalysis wind data. As boundary conditions, the NRT system uses the 2D wave spectra from the Copernicus Marine GLOBAL forecast system, whereas the MY system is nested to the GLOBAL reanalysis.\nThe product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum; fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra; and the highest wave variables, such as maximum crest height and maximum crest-to-trough height. Additionally, climatological parameters of significant wave height (VHM0) and zero -crossing wave period (VTM02) are delivered for the time interval 1993-2016.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (Product)''': \nhttps://doi.org/10.48670/moi-00030", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-multiyear-wav-005-006:cmems-mod-ibi-wav-my-0.027deg-climatology-p1m-m-202311,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,eo:mo:dat:ibi-multiyear-wav-005-006:cmems-mod-ibi-wav-my-0.027deg-pt1h-i-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-30", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis"}, "EO:MO:DAT:INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032:cmems_obs-ins_bal_phybgcwav_mynrt_na_irr_202311": {"abstract": "'''Short description:'''\nBaltic Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00032", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,direction-of-sea-water-velocity,eo:mo:dat:insitu-bal-phybgcwav-discrete-mynrt-013-032:cmems-obs-ins-bal-phybgcwav-mynrt-na-irr-202311,in-situ-observation,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,sea-surface-wave-from-direction,sea-surface-wave-mean-period,sea-surface-wave-significant-height,sea-water-practical-salinity,sea-water-speed,sea-water-temperature,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea- In Situ Near Real Time Observations"}, "EO:MO:DAT:INSITU_GLO_BGC_DISCRETE_MY_013_046:cmems_obs-ins_glo_bgc-nut_my_na_irr_202411": {"abstract": "'''Short description:'''\nFor the Global Ocean- In-situ observation delivered in delayed mode. This In Situ delayed mode product integrates the best available version of in situ oxygen, chlorophyll / fluorescence and nutrients data.\n\n'''DOI (product) :''' \nhttps://doi.org/10.17882/86207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-bgc-discrete-my-013-046:cmems-obs-ins-glo-bgc-nut-my-na-irr-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-silicate-in-sea-water,moles-of-oxygen-per-unit-mass-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2021-12-31", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Delayed Mode Biogeochemical product"}, "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_PT1H_202411": {"abstract": "'''Short description:'''\n\nThis product integrates sea level observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from the Global telecommunication system (GTS) used by the Met Offices.\n\n'''DOI (product) :''' \nhttps://doi.org/10.17882/93670", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ssh-discrete-my-013-053:cmems-obs-ins-glo-phy-ssh-my-na-pt1h-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,near-real-time,non-tidal-elevation-of-sea-surface-height,not-applicable,oceanographic-geographical-features,tidal-sea-surface-height-above-reference-datum,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Delayed Mode Sea level product"}, "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_irr_202411": {"abstract": "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_irr_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ssh-discrete-my-013-053:cmems-obs-ins-glo-phy-ssh-my-na-irr-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,near-real-time,non-tidal-elevation-of-sea-surface-height,not-applicable,oceanographic-geographical-features,tidal-sea-surface-height-above-reference-datum,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Delayed Mode Sea level product"}, "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_ibi_phy-ssh_my_tide-surge_PT1H_202411": {"abstract": "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_ibi_phy-ssh_my_tide-surge_PT1H_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ssh-discrete-my-013-053:cmems-obs-ins-ibi-phy-ssh-my-tide-surge-pt1h-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,near-real-time,non-tidal-elevation-of-sea-surface-height,not-applicable,oceanographic-geographical-features,tidal-sea-surface-height-above-reference-datum,water-surface-height-above-reference-datum,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-12-31", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Delayed Mode Sea level product"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_MY_013_052:cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M_202411": {"abstract": "'''Short description:''''\nGlobal Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the reprocessed in-situ global product CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) using the ISAS software. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n'''DOI (product) :''' \nhttps://doi.org/10.17882/46219", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ts-oa-my-013-052:cmems-obs-ins-glo-phy-temp-sal-my-cora-oa-p1m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1960-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_NRT_013_002:cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M_202411": {"abstract": "'''Short description:'''\nFor the Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the in-situ near real time database are produced monthly. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a support for localized experience (cruises), providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00037", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-phy-ts-oa-nrt-013-002:cmems-obs-ins-glo-phy-temp-sal-nrt-oa-p1m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2015-01-15", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean- Real time in-situ observations objective analysis"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_adcp_irr_202411": {"abstract": "\"'Short description: '''\n\nGlobal Ocean - This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface and subsurface currents. Current data from 4 different types of instruments are distributed: \n* The NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML) Surface Velocity Program (SVP) Drifter\u2019s reprocessing from 1990. It provides the drifter's position, velocity and includes temperature measurements. In addition, a wind slippage correction is provided from 1993. \n* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency (HF) radars that are part of the European HF radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata. \n* The zonal and meridional velocities, at parking depth (mostly around 1000m) and at the surface, calculated along the trajectories of the floats which are part of the Argo Program. \n* The velocity profiles within the water column coming from Acoustic Doppler Current Profiler (vessel mounted ADCP, Moored ADCP, saildrones) platforms\n\n'''DOI (product) :'''\nhttps://doi.org/10.17882/86236", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:insitu-glo-phy-uv-discrete-my-013-044:cmems-obs-ins-glo-phy-cur-my-adcp-irr-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,not-applicable,oceanographic-geographical-features,sea-water-temperature,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-30", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_argo_irr_202411": {"abstract": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_argo_irr_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:insitu-glo-phy-uv-discrete-my-013-044:cmems-obs-ins-glo-phy-cur-my-argo-irr-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,not-applicable,oceanographic-geographical-features,sea-water-temperature,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-30", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_drifter_PT1H_202411": {"abstract": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_drifter_PT1H_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:insitu-glo-phy-uv-discrete-my-013-044:cmems-obs-ins-glo-phy-cur-my-drifter-pt1h-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,not-applicable,oceanographic-geographical-features,sea-water-temperature,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-30", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_glider_irr_202411": {"abstract": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_glider_irr_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:insitu-glo-phy-uv-discrete-my-013-044:cmems-obs-ins-glo-phy-cur-my-glider-irr-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,not-applicable,oceanographic-geographical-features,sea-water-temperature,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-30", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents"}, "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_PT1H_202411": {"abstract": "'''Short description:'''\n\nThese products integrate wave observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from National Data Centers (NODCs) and JCOMM global systems (OceanSITES, DBCP) and the Global telecommunication system (GTS) used by the Met Offices.\n\n'''DOI (product) :''' \nhttps://doi.org/10.17882/70345", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-wav-discrete-my-013-045:cmems-obs-ins-glo-wav-my-na-pt1h-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,sea-surface-wave-mean-period,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-30", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Delayed Mode Wave product"}, "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_irr_202411": {"abstract": "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_irr_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:insitu-glo-wav-discrete-my-013-045:cmems-obs-ins-glo-wav-my-na-irr-202411,global-ocean,in-situ-observation,level-2,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,sea-surface-wave-mean-period,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-30", "missionStartDate": "1990-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Delayed Mode Wave product"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-bio-anfc-4.2km-p1d-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-bio-anfc-4.2km-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-car-anfc-4.2km-p1d-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-car-anfc-4.2km-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-co2-anfc-4.2km-p1d-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-co2-anfc-4.2km-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-nut-anfc-4.2km-p1d-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-nut-anfc-4.2km-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202211": {"abstract": "'''Short Description'''\nThe biogeochemical analysis and forecasts for the Mediterranean Sea at 1/24\u00b0 of horizontal resolution (ca. 4 km) are produced by means of the MedBFM4 model system. MedBFM4, which is run by OGS (IT), consists of the coupling of the multi-stream atmosphere radiative model OASIM, the multi-stream in-water radiative and tracer transport model OGSTM_BIOPTIMOD v4.3, and the biogeochemical flux model BFM v5. Additionally, MedBFM4 features the 3D variational data assimilation scheme 3DVAR-BIO v3.3 with the assimilation of surface chlorophyll (CMEMS-OCTAC NRT product) and of vertical profiles of chlorophyll, nitrate and oxygen (BGC-Argo floats provided by CORIOLIS DAC).\nThe biogeochemical MedBFM system, which is forced by the NEMO-OceanVar model (MEDSEA_ANALYSIS_FORECAST_PHY_006_013 product run by CMCC), produces one day of hindcast and ten days of forecast (every day) and seven days of analysis (weekly on Tuesday).\n\nSalon, S., Cossarini, G., Bolzon, G., Feudale, L., Lazzari, P., Teruzzi, A., Solidoro, C., Crise, A., 2019. Marine Ecosystem forecasts: skill performance of the CMEMS Mediterranean Sea model system. Ocean Sci. Discuss. 1\u201335. https://doi.org/10.5194/os-2018-145\n\n''Product Citation'': Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n''DOI (Product)'': https://doi.org/10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbfm4", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-optics-anfc-4.2km-p1d-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-optics-anfc-4.2km-p1m-m-202211,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202311": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202311", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-pft-anfc-4.2km-p1d-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202311": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202311", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-bgc-006-014:cmems-mod-med-bgc-pft-anfc-4.2km-p1m-m-202311,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanoflagellates-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-diatoms-expressed-as-carbon-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nanoflagellates-expressed-as-carbon-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-picophytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-water,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,nutrients-(o2-n-p),oceanographic-geographical-features,satellite-chlorophyll,sea-binary-mask,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water-490,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-11-29", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "'''Short Description'''\nThe physical component of the Mediterranean Forecasting System (Med-Physics) is a coupled hydrodynamic-wave model implemented over the whole Mediterranean Basin including tides. The model horizontal grid resolution is 1/24\u02da (ca. 4 km) and has 141 unevenly spaced vertical levels.\nThe hydrodynamics are supplied by the Nucleous for European Modelling of the Ocean NEMO (v4.2) and include the representation of tides, while the wave component is provided by Wave Watch-III (v6.07) coupled through OASIS; the model solutions are corrected by a 3DVAR assimilation scheme (OceanVar) for temperature and salinity vertical profiles and along track satellite Sea Level Anomaly observations.\n\n''Product Citation'': Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n''DOI (Product)'': https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-4.2km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-cur-anfc-detided-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-mld-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-mld-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-mld-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-sal-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-4.2km-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-ssh-anfc-detided-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-tem-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-wcur-anfc-4.2km-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-analysisforecast-phy-006-013:cmems-mod-med-phy-wcur-anfc-4.2km-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,near-real-time,northward-sea-water-velocity,northward-sea-water-velocity-assuming-no-tided,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-surface-height-above-geoid-assuming-no-tided,sea-surface-height-above-geoid-detided,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_WAV_006_017:cmems_mod_med_wav_anfc_4.2km_PT1H-i_202311": {"abstract": "'''Short description:'''\n \nMEDSEA_ANALYSISFORECAST_WAV_006_017 is the nominal wave product of the Mediterranean Sea Forecasting system, composed by hourly wave parameters at 1/24\u00ba horizontal resolution covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The waves forecast component (Med-WAV system) is a wave model based on the WAM Cycle 6. The Med-WAV modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins and the model solutions are corrected by an optimal interpolation data assimilation scheme of all available along track satellite significant wave height observations. The atmospheric forcing is provided by the operational ECMWF Numerical Weather Prediction model and the wave model is forced with hourly averaged surface currents and sea level obtained from MEDSEA_ANALYSISFORECAST_PHY_006_013 at 1/24\u00b0 resolution. The model uses wave spectra for Open Boundary Conditions from GLOBAL_ANALYSIS_FORECAST_WAV_001_027 product. The wave system includes 2 forecast cycles providing twice per day a Mediterranean wave analysis and 10 days of wave forecasts.\n\n''Product Citation'': Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (product)''': https://doi.org/10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-analysisforecast-wav-006-017:cmems-mod-med-wav-anfc-4.2km-pt1h-i-202311,forecast,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-maximum-crest-height,sea-surface-wave-maximum-height,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Analysis and Forecast"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-bio-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-bio-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-car-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_myint_4.2km_P1M-m_202112": {"abstract": "'''Short Description'''\nThe Mediterranean Sea biogeochemical reanalysis at 1/24\u00b0 of horizontal resolution (ca. 4 km) covers the period from Jan 1999 to 1 month to the present and is produced by means of the MedBFM3 model system. MedBFM3, which is run by OGS (IT), includes the transport model OGSTM v4.0 coupled with the biogeochemical flux model BFM v5 and the variational data assimilation module 3DVAR-BIO v2.1 for surface chlorophyll. MedBFM3 is forced by the physical reanalysis (MEDSEA_MULTIYEAR_PHY_006_004 product run by CMCC) that provides daily forcing fields (i.e., currents, temperature, salinity, diffusivities, wind and solar radiation). The ESA-CCI database of surface chlorophyll concentration (CMEMS-OCTAC REP product) is assimilated with a weekly frequency. \n\nCossarini, G., Feudale, L., Teruzzi, A., Bolzon, G., Coidessa, G., Solidoro C., Amadio, C., Lazzari, P., Brosich, A., Di Biagio, V., and Salon, S., 2021. High-resolution reanalysis of the Mediterranean Sea biogeochemistry (1999-2019). Frontiers in Marine Science. Front. Mar. Sci. 8:741486.doi: 10.3389/fmars.2021.741486\n\n''Product Citation'': Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n''DOI (Product)'': https://doi.org/10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3\n\n''DOI (Interim dataset)'':\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-car-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-co2-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-co2-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-nut-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-nut-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-pft-myint-4.2km-p1m-m-202112,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:cmems-mod-med-bgc-plankton-my-4.2km-p1y-m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-bio-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-bio-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-car-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-car-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-co2-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-co2-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-nut-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-nut-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-pft-rean-d-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eo:mo:dat:medsea-multiyear-bgc-006-008:med-ogs-pft-rean-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,model-level-number-at-sea-floor,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-floor-depth-below-geoid,sea-water-alkalinity-expressed-as-mole-equivalent,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Biogeochemistry Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-cur-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-hflux-my-4.2km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-hflux-my-4.2km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-mflux-my-4.2km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-mflux-my-4.2km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-mld-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-sal-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-ssh-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-tem-my-4.2km-p1y-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-wflux-my-4.2km-p1d-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-wflux-my-4.2km-p1m-m-202411,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:cmems-mod-med-phy-my-4.2km-climatology-p1m-m-202211,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-h_202012": {"abstract": "'''Short description:'''\n\nThe Med MFC physical multiyear product is generated by a numerical system composed of an hydrodynamic model, supplied by the Nucleous for European Modelling of the Ocean (NEMO) and a variational data assimilation scheme (OceanVAR) for temperature and salinity vertical profiles and satellite Sea Level Anomaly along track data. It contains a reanalysis dataset and an interim dataset which covers the period after the reanalysis until 1 month before present. The model horizontal grid resolution is 1/24\u02da (ca. 4-5 km) and the unevenly spaced vertical levels are 141. \n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products\n\n'''DOI (Product)''': \nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1\n\n'''DOI (Interim dataset)''':\nhttps://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-rean-h-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-cur-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-mld-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-mld-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-mld-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-sal-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-sal-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-sal-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-rean-h-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-ssh-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-tem-int-m-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-tem-rean-d-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012", "instrument": null, "keywords": "cell-thickness,coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:medsea-multiyear-phy-006-004:med-cmcc-tem-rean-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,mediterranean-sea,model-level-number-at-sea-floor,multi-year,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1987-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Physics Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_my_4.2km-climatology_P1M-m_202311": {"abstract": "'''Short description:'''\n\nMEDSEA_MULTIYEAR_WAV_006_012 is the multi-year wave product of the Mediterranean Sea Waves forecasting system (Med-WAV). It contains a Reanalysis dataset, an Interim dataset covering the period after the reanalysis until 1 month before present and a monthly climatological dataset (reference period 1993-2016). The Reanalysis dataset is a multi-year wave reanalysis starting from January 1993, composed by hourly wave parameters at 1/24\u00b0 horizontal resolution, covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The Med-WAV modelling system is based on wave model WAM 4.6.2 and has been developed as a nested sequence of two computational grids (coarse and fine) to ensure that swell propagating from the North Atlantic (NA) towards the strait of Gibraltar is correctly entering the Mediterranean Sea. The coarse grid covers the North Atlantic Ocean from 75\u00b0W to 10\u00b0E and from 70\u00b0 N to 10\u00b0 S in 1/6\u00b0 resolution while the nested fine grid covers the Mediterranean Sea from 18.125\u00b0 W to 36.2917\u00b0 E and from 30.1875\u00b0 N to 45.9792\u00b0 N with a 1/24\u00b0 resolution. The modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins. The wave system also includes an optimal interpolation assimilation scheme assimilating significant wave height along track satellite observations available through CMEMS and it is forced with daily averaged currents from Med-Physics and with 1-h, 0.25\u00b0 horizontal-resolution ERA5 reanalysis 10m-above-sea-surface winds from ECMWF.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169\n\n'''DOI (product)''': \nhttps://doi.org/10.25423/cmcc/medsea_multiyear_wav_006_012\n\n'''DOI (Interim dataset)''':\nhttps://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-multiyear-wav-006-012:cmems-mod-med-wav-my-4.2km-climatology-p1m-m-202311,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-multiyear-wav-006-012:cmems-mod-med-wav-myint-4.2km-pt1h-i-202112,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Reanalysis"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411": {"abstract": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:medsea-multiyear-wav-006-012:med-hcmr-wav-rean-h-202411,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,numerical-model,oceanographic-geographical-features,sea-floor-depth-below-geoid,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,significant-wave-height-(swh),weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea Waves Reanalysis"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg-climatology_P1M-m_202411": {"abstract": "'''Short description:'''\n\nThis product consists of 3D fields of Particulate Organic Carbon (POC), Particulate Backscattering coefficient (bbp) and Chlorophyll-a concentration (Chla) at depth. The reprocessed product is provided at 0.25\u00b0x0.25\u00b0 horizontal resolution, over 36 levels from the surface to 1000 m depth. \nA neural network method estimates both the vertical distribution of Chla concentration and of particulate backscattering coefficient (bbp), a bio-optical proxy for POC, from merged surface ocean color satellite measurements with hydrological properties and additional relevant drivers. \n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00046\n\n'''Product Citation:''' \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:multiobs-glo-bio-bgc-3d-rep-015-010:cmems-obs-mob-glo-bgc-chl-poc-my-0.25deg-climatology-p1m-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,modelling-data,multi-year,none,oceanographic-geographical-features,satellite-observation,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411", "instrument": null, "keywords": "/cross-discipline/rate-measurements,atlantic-ocean,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:multiobs-glo-bio-bgc-3d-rep-015-010:cmems-obs-mob-glo-bgc-chl-poc-my-0.25deg-p7d-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-particulate-organic-matter-expressed-as-carbon-in-sea-water,modelling-data,multi-year,none,oceanographic-geographical-features,satellite-observation,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1998-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411", "instrument": null, "keywords": "eo:mo:dat:multiobs-glo-bio-carbon-surface-mynrt-015-008:cmems-obs-mob-glo-bgc-car-my-irr-i-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411", "instrument": null, "keywords": "eo:mo:dat:multiobs-glo-bio-carbon-surface-mynrt-015-008:cmems-obs-mob-glo-bgc-car-nrt-irr-i-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThis product is a L4 REP and NRT global total velocity field at 0m and 15m together wiht its individual components (geostrophy and Ekman) and related uncertainties. It consists of the zonal and meridional velocity at a 1h frequency and at 1/4 degree regular grid. The total velocity fields are obtained by combining CMEMS satellite Geostrophic surface currents and modelled Ekman currents at the surface and 15m depth (using ERA5 wind stress in REP and ERA5* in NRT). 1 hourly product, daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program).\n\n'''Product Citation:'''\nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00327", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eastward-sea-water-velocity-due-to-ekman-drift-standard-error,eastward-sea-water-velocity-standard-error,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-my-0.25deg-p1d-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,northward-sea-water-velocity-due-to-ekman-drift-standard-error,northward-sea-water-velocity-standard-error,numerical-model,oceanographic-geographical-features,satellite-observation,surface-geostophic-eastward-sea-water-velocity-standard-error,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eastward-sea-water-velocity-due-to-ekman-drift-standard-error,eastward-sea-water-velocity-standard-error,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-my-0.25deg-p1m-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,northward-sea-water-velocity-due-to-ekman-drift-standard-error,northward-sea-water-velocity-standard-error,numerical-model,oceanographic-geographical-features,satellite-observation,surface-geostophic-eastward-sea-water-velocity-standard-error,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eastward-sea-water-velocity-due-to-ekman-drift-standard-error,eastward-sea-water-velocity-standard-error,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-my-0.25deg-pt1h-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,northward-sea-water-velocity-due-to-ekman-drift-standard-error,northward-sea-water-velocity-standard-error,numerical-model,oceanographic-geographical-features,satellite-observation,surface-geostophic-eastward-sea-water-velocity-standard-error,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eastward-sea-water-velocity-due-to-ekman-drift-standard-error,eastward-sea-water-velocity-standard-error,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-nrt-0.25deg-p1d-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,northward-sea-water-velocity-due-to-ekman-drift-standard-error,northward-sea-water-velocity-standard-error,numerical-model,oceanographic-geographical-features,satellite-observation,surface-geostophic-eastward-sea-water-velocity-standard-error,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eastward-sea-water-velocity-due-to-ekman-drift-standard-error,eastward-sea-water-velocity-standard-error,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-nrt-0.25deg-p1m-m-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,northward-sea-water-velocity-due-to-ekman-drift-standard-error,northward-sea-water-velocity-standard-error,numerical-model,oceanographic-geographical-features,satellite-observation,surface-geostophic-eastward-sea-water-velocity-standard-error,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eastward-sea-water-velocity-due-to-ekman-drift,eastward-sea-water-velocity-due-to-ekman-drift-standard-error,eastward-sea-water-velocity-standard-error,eo:mo:dat:multiobs-glo-phy-mynrt-015-003:cmems-obs-mob-glo-phy-cur-nrt-0.25deg-pt1h-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,northward-sea-water-velocity,northward-sea-water-velocity-due-to-ekman-drift,northward-sea-water-velocity-due-to-ekman-drift-standard-error,northward-sea-water-velocity-standard-error,numerical-model,oceanographic-geographical-features,satellite-observation,surface-geostophic-eastward-sea-water-velocity-standard-error,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-asc_P1D_202411": {"abstract": "'''Short description:'''\n\nThe product MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014 is a reformatting and a simplified version of the CATDS L3 product called \u201c2Q\u201d or \u201cL2Q\u201d. it is an intermediate product, that provides, in daily files, SSS corrected from land-sea contamination and latitudinal bias, with/without rain freshening correction.\n\n'''DOI (product) :''' \nhttps://doi.org/10.1016/j.rse.2016.02.061", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-sss-l3-mynrt-015-014:cmems-obs-mob-glo-phy-sss-mynrt-smos-asc-p1d-202411,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-sss-l3-mynrt-015-014:cmems-obs-mob-glo-phy-sss-mynrt-smos-des-p1d-202411,global-ocean,in-situ-observation,level-3,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SMOS CATDS Qualified (L2Q) Sea Surface Salinity product"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L4_MY_015_015:cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W_202406": {"abstract": "'''Short description:'''\n\nThe product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called \u201cSMOS-OI\u201d. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information.\n\nKolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/\n\n'''DOI (product) :''' \nhttps://doi.org/10.1175/JTECH-D-20-0093.1", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-sss-l4-my-015-015:cmems-obs-mob-glo-phy-sss-my-multi-oi-p1w-202406,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,sea-surface-temperature,sea-water-conservative-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2010-05-31", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "SSS SMOS/SMAP L4 OI - LOPS-v2023"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-my-multi-p1d-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1M_202311": {"abstract": "'''Short description:'''\n\nThis product consits of daily global gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/8\u00b0 of resolution, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA\u2019s Soil Moisture Active Passive (SMAP) and ESA\u2019s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite SST information. The product was developed by the Consiglio Nazionale delle Ricerche (CNR) and includes 4 datasets:\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1D, which provides near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1M, which provides near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy-sss_my_multi_P1D, which provides multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy-sss_my_multi_P1M, which provides multi-year reprocessed (REP) monthly data \n\n'''Product citation''': \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00051", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-my-multi-p1m-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-nrt-multi-p1d-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-s-surface-mynrt-015-013:cmems-obs-mob-glo-phy-sss-nrt-multi-p1m-202311,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,oceanographic-geographical-features,satellite-observation,sea-surface-density,sea-surface-salinity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-nrt-monthly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-weekly_202012": {"abstract": "'''Short description:'''\nYou can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing. It consists of 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/4 degree regular grid and on 50 depth levels from the surface down to the bottom. The product includes 4 datasets: \n* dataset-armor-3d-nrt-weekly, which delivers near-real-time (NRT) weekly data\n* dataset-armor-3d-nrt-monthly, which delivers near-real-time (NRT) monthly data\n* dataset-armor-3d-rep-weekly, which delivers multi-year reprocessed (REP) weekly data \n* dataset-armor-3d-rep-monthly, which delivers multi-year reprocessed (REP) monthly data\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00052\n\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-nrt-weekly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-rep-monthly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012": {"abstract": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:multiobs-glo-phy-tsuv-3d-mynrt-015-012:dataset-armor-3d-rep-weekly-202012,geopotential-height,geostrophic-eastward-sea-water-velocity,geostrophic-northward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,near-real-time,none,ocean-mixed-layer-thickness,oceanographic-geographical-features,satellite-observation,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_W_3D_REP_015_007:cmems_obs-mob_glo_phy-cur_my_0.25deg_P7D-i_202411": {"abstract": "'''Short description''':\n\nYou can find here the OMEGA3D observation-based quasi-geostrophic vertical and horizontal ocean currents developed by the Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4\u00b0 horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_REP_015_002 which corresponds to former version of MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012) and ERA-Interim surface fluxes. \n\n'''DOI (product) :''' \nhttps://commons.datacite.org/doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007\n\n\n'''Product citation''': \nPlease refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:multiobs-glo-phy-w-3d-rep-015-007:cmems-obs-mob-glo-phy-cur-my-0.25deg-p7d-i-202411,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,northward-sea-water-velocity,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2018-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D)"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThe NWSHELF_ANALYSISFORECAST_BGC_004_002 is produced by a coupled physical-biogeochemical model, implemented over the North East Atlantic and Shelf Seas at 1/20 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated weekly, providing 10-day forecast of the main biogeochemical variables.\nProducts are provided as daily and monthly means.\n\n'''Product Citation''':\nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00056", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-optics-anfc-0.027deg-p1d-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-optics-anfc-0.027deg-p1m-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-anfc-0.027deg-3d-p1d-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,e1t,e2t,e3t,eo:mo:dat:nwshelf-analysisforecast-bgc-004-002:cmems-mod-nws-bgc-anfc-0.027deg-3d-p1m-m-202411,euphotic-zone-depth,forecast,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-ammonium-in-sea-water,mole-concentration-of-dissolved-inorganic-carbon-in-sea-water,mole-concentration-of-dissolved-iron-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,mole-concentration-of-silicate-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watermass-concentration-of-chlorophyll-a-in-sea-water,mole-concentration-of-zooplankton-expressed-as-carbon-in-sea-watersea-floor-depth-below-geoid,near-real-time,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-binary-mask,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-30", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "'''Short description:'''\n\nThe NWSHELF_ANALYSISFORECAST_PHY_004_013 is produced by a hydrodynamic model with tides, implemented over the North East Atlantic and Shelf Seas at 1/36 degrees of horizontal resolution and 50 vertical levels.\nThe product is updated daily, providing 5-day forecast for temperature, salinity, currents, sea level and mixed layer depth.\nProducts are provided at quarter-hourly, hourly, daily de-tided, and monthly frequency.\n\n'''Product Citation''': \nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00054", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-cur-anfc-detided-0.027deg-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-cur-anfc-detided-0.027deg-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-ssh-anfc-detided-0.027deg-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-ssh-anfc-detided-0.027deg-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-wcur-anfc-0.027deg-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-wcur-anfc-0.027deg-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-2d-pt15m-i-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-2d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-3d-p1d-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-3d-p1m-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411", "instrument": null, "keywords": "coastal-marine-environment,depth,deptho-lev-interp,eastward-sea-water-velocity,eo:mo:dat:nwshelf-analysisforecast-phy-004-013:cmems-mod-nws-phy-anfc-0.027deg-3d-pt1h-m-202411,forecast,in-situ-ts-profiles,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-binary-mask,sea-floor-depth-below-geoid,sea-level,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411": {"abstract": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-analysisforecast-wav-004-014:cmems-mod-nws-wav-anfc-0.027deg-pt1h-i-202411,forecast,level-4,marine-resources,marine-safety,near-real-time,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.05deg_PT1H-i_202309": {"abstract": "'''Short description:'''\n\nThe NWSHELF_ANALYSISFORECAST_WAV_004_014 is produced by a wave model system based on MFWAV, implemented over the North East Atlantic and Shelf Seas at 1/20 degrees of horizontal resolution forced by ECMWF wind data. The system assimilates significant wave height altimeter data and spectral data, and it is forced by currents provided by the [ ref t the physical system] ocean circulation system.\nThe product is updated twice a day, providing 10-day forecast of wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state, and wind-state, and swell components. \nProducts are provided at hourly frequency \n\n'''Product Citation''':\nPlease refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169]\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00055", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-analysisforecast-wav-004-014:cmems-mod-nws-wav-anfc-0.05deg-pt1h-i-202309,forecast,level-4,marine-resources,marine-safety,near-real-time,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-chl-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-chl-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-chl-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-kd-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-kd-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-kd-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-no3-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-no3-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-no3-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-o2-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-o2-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-o2-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-diato-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-diato-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-dino-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m_202012": {"abstract": "'''Short Description:'''\n\nThe ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butenschön et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009]. Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data.\n\nThe description of the model and its configuration, including the products validation is provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-011.pdf CMEMS-NWS-QUID-004-011]. \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually, providing a six-month extension of the time series. See [http://resources.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-009_011.pdf CMEMS-NWS-PUM-004-009_011] for details.\n\n'''Associated products:'''\n\nThis model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009].\nAn analysis-forecast product is available from: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011 NWSHELF_MULTIYEAR_BGC_004_011].\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00058", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-dino-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-nano-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-nano-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-pico-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-my-7km-3d-pico-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-diato-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-dino-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-nano-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pft-myint-7km-3d-pico-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-ph-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-ph-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-ph-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-phyc-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-phyc-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-phyc-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-po4-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-po4-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-po4-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pp-my-7km-3d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pp-my-7km-3d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-pp-myint-7km-3d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-spco2-my-7km-2d-p1d-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-spco2-my-7km-2d-p1m-m-202012,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-multiyear-bgc-004-011:cmems-mod-nws-bgc-spco2-myint-7km-2d-p1m-m-202105,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mole-concentration-of-dissolved-molecular-oxygen-in-sea-water,mole-concentration-of-nitrate-in-sea-water,mole-concentration-of-phosphate-in-sea-water,mole-concentration-of-phytoplankton-expressed-as-carbon-in-sea-water,multi-year,net-primary-production-of-biomass-expressed-as-carbon-per-unit-volume-in-sea-water,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,satellite-chlorophyll,sea-water-ph-reported-on-total-scale,surface-partial-pressure-of-carbon-dioxide-in-sea-water,volume-beam-attenuation-coefficient-of-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-my-7km-2d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-my-7km-2d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-bottomt-myint-7km-2d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-my-7km-2d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-my-7km-2d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-mld-myint-7km-2d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1D-m_202012": {"abstract": "'''Short Description:'''\n\nThe ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution. \nThe ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026 GLOBAL_REANALYSIS_PHY_001_026] and at the Baltic boundary by the [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_PHY_003_011 BALTICSEA_REANALYSIS_PHY_003_011]. The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. \n\nFurther details of the model, including the product validation are provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-009.pdf CMEMS-NWS-QUID-004-009]. \n\nProducts are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually provinding six-month extension of the time series.\n\nSee [http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-009_011.pdf CMEMS-NWS-PUM-004-009_011] for further details.\n\n'''Associated products:'''\n\nThis model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011]. An analysis-forecast product is available from [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001 NWSHELF_ANALYSISFORECAST_PHY_LR_004_011].\nThe product is updated biannually provinding six-month extension of the time series.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00059", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-s-my-7km-3d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-s-my-7km-3d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-s-myint-7km-3d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-my-7km-2d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-my-7km-2d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-ssh-myint-7km-2d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-sss-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-sst-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-t-my-7km-3d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-t-my-7km-3d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-t-myint-7km-3d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-my-7km-2d-pt1h-i-202112,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-my-7km-3d-p1d-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-my-7km-3d-p1m-m-202012,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105": {"abstract": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105", "instrument": null, "keywords": "coastal-marine-environment,eastward-sea-water-velocity,eo:mo:dat:nwshelf-multiyear-phy-004-009:cmems-mod-nws-phy-uv-myint-7km-3d-p1m-m-202105,in-situ-ts-profiles,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,northward-sea-water-velocity,numerical-model,ocean-mixed-layer-thickness-defined-by-sigma-theta,oceanographic-geographical-features,sea-surface-height-above-geoid,sea-water-potential-temperature,sea-water-potential-temperature-at-sea-floor,sea-water-salinity,sst,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Ocean Physics Reanalysis"}, "EO:MO:DAT:NWSHELF_REANALYSIS_WAV_004_015:MetO-NWS-WAV-RAN_202007": {"abstract": "'''Short description:'''\n\nThis product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013 NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013]. The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ECMWF ERA-5] Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-015.pdf CMEMS-NWS-PUM-004-015] for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004-015.pdf CMEMS-NWS-QUID-004-015].\nThe product is updated biannually provinding six-month extension of the time series.\n\n'''Associated products:'''\n\n[https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014 NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014].\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00060", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:nwshelf-reanalysis-wav-004-015:meto-nws-wav-ran-202007,level-4,marine-resources,marine-safety,multi-year,none,north-west-shelf-seas,numerical-model,oceanographic-geographical-features,sea-surface-primary-swell-wave-from-direction,sea-surface-primary-swell-wave-mean-period,sea-surface-primary-swell-wave-significant-height,sea-surface-secondary-swell-wave-from-direction,sea-surface-secondary-swell-wave-mean-period,sea-surface-secondary-swell-wave-significant-height,sea-surface-wave-from-direction,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-mean-period-from-variance-spectral-density-inverse-frequency-moment,sea-surface-wave-mean-period-from-variance-spectral-density-second-frequency-moment,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,sea-surface-wave-stokes-drift-x-velocity,sea-surface-wave-stokes-drift-y-velocity,sea-surface-wind-wave-from-direction,sea-surface-wind-wave-mean-period,sea-surface-wind-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1980-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic- European North West Shelf- Wave Physics Reanalysis"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l3-my-009-123:cmems-obs-oc-arc-bgc-plankton-my-l3-multi-4km-p1d-202311,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"abstract": "'''Short description:'''\n\nFor the '''Arctic''' Ocean '''Satellite Observations''', Italian National Research Council (CNR \u2013 Rome, Italy) is providing '''Bio-Geo_Chemical (BGC)''' products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the '''\"multi\"''' products and S3A & S3B only for the '''\"OLCI\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Diffuse Attenuation ('''KD490''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''4 km''' (multi) or '''300 m''' (OLCI).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00292", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l3-my-009-123:cmems-obs-oc-arc-bgc-reflectance-my-l3-multi-4km-p1d-202311,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l3-my-009-123:cmems-obs-oc-arc-bgc-transp-my-l3-multi-4km-p1d-202311,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,rrs400,rrs412,rrs443,rrs490,rrs510,rrs560,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L4_MY_009_124:cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M_202311": {"abstract": "'''Short description:'''\n\nFor the '''Arctic''' Ocean '''Satellite Observations''', Italian National Research Council (CNR \u2013 Rome, Italy) is providing '''Bio-Geo_Chemical (BGC)''' products.\n* Upstreams: OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the '''\"multi\"''' products , and S3A & S3B only for the '''\"OLCI\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Diffuse Attenuation ('''KD490''')\n\n\n* Temporal resolutions: '''monthly'''.\n* Spatial resolutions: '''4 km''' (multi) or '''300 meters''' (OLCI).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00293", "instrument": null, "keywords": "arctic-ocean,chl,coastal-marine-environment,eo:mo:dat:oceancolour-arc-bgc-l4-my-009-124:cmems-obs-oc-arc-bgc-plankton-my-l4-multi-4km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Colour Plankton MY L4 daily climatology and monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-optics-my-l3-multi-1km-p1d-202311,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-plankton-my-l3-multi-1km-p1d-202411,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-plankton-my-l3-olci-1km-p1d-202411,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-plankton-my-l3-olci-300m-p1d-202303,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-reflectance-my-l3-multi-1km-p1d-202311,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D_202303": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''1 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00286", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-reflectance-my-l3-olci-300m-p1d-202303,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-my-009-113:cmems-obs-oc-atl-bgc-transp-my-l3-multi-1km-p1d-202311,global-ocean,kd490,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''1 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00284", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-optics-nrt-l3-multi-1km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-plankton-nrt-l3-multi-1km-p1d-202411,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-plankton-nrt-l3-olci-1km-p1d-202411,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-plankton-nrt-l3-olci-300m-p1d-202303,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-reflectance-nrt-l3-multi-1km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-reflectance-nrt-l3-olci-300m-p1d-202303,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l3-nrt-009-111:cmems-obs-oc-atl-bgc-transp-nrt-l3-multi-1km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-my-009-118:cmems-obs-oc-atl-bgc-plankton-my-l4-gapfree-multi-1km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-my-009-118:cmems-obs-oc-atl-bgc-plankton-my-l4-multi-1km-p1m-202411,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: '''1 km'''.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00289", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-my-009-118:cmems-obs-oc-atl-bgc-pp-my-l4-multi-1km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-nrt-009-116:cmems-obs-oc-atl-bgc-plankton-nrt-l4-gapfree-multi-1km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-nrt-009-116:cmems-obs-oc-atl-bgc-plankton-nrt-l4-multi-1km-p1m-202411,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: '''1 km'''.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00288", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-atl-bgc-l4-nrt-009-116:cmems-obs-oc-atl-bgc-pp-nrt-l4-multi-1km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,pft,pp,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\n*cmems_obs_oc_bal_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00079", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-hr-l3-nrt-009-202:cmems-obs-oc-bal-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n*cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00080", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-hr-l4-nrt-009-208:cmems-obs-oc-bal-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-optics-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-plankton-my-l3-multi-1km-p1d-202411,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L3_MY\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00296", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-plankton-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-reflectance-my-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-reflectance-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-transp-my-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-my-009-133:cmems-obs-oc-bal-bgc-transp-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-optics-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-plankton-nrt-l3-olci-300m-p1d-202411,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': OLCI-S3A & S3B \n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 300 meters \n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L3_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00294", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-reflectance-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l3-nrt-009-131:cmems-obs-oc-bal-bgc-transp-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-plankton-my-l4-multi-1km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly\n\n'''Spatial resolution''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L4_MY\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00308", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-plankton-my-l4-olci-300m-p1m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-pp-my-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-my-009-134:cmems-obs-oc-bal-bgc-pp-my-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Multiyear Ocean Colour Plankton monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_NRT_009_132:cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M_202411": {"abstract": "'''Short description:'''\n\nFor the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)\n\n'''Upstreams''': OLCI-S3A & S3B \n\n'''Temporal resolution''': monthly \n\n'''Spatial resolution''': 300 meters \n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BAL_BGC_L4_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00295", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:oceancolour-bal-bgc-l4-nrt-009-132:cmems-obs-oc-bal-bgc-plankton-nrt-l4-olci-300m-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided within 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\n*cmems_obs_oc_blk_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00086", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-hr-l3-nrt-009-206:cmems-obs-oc-blk-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n*cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00087", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-hr-l4-nrt-009-212:cmems-obs-oc-blk-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-optics-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-plankton-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-plankton-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-reflectance-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm \n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_BLK_BGC_L3_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00303", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-reflectance-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-transp-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-my-009-153:cmems-obs-oc-blk-bgc-transp-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) \n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BLK_BGC_L3_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00301", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-optics-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-plankton-nrt-l3-multi-1km-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-plankton-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-reflectance-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-reflectance-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-transp-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l3-nrt-009-151:cmems-obs-oc-blk-bgc-transp-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-gapfree-multi-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-multi-1km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated '''gap-free''' Chl concentration (to provide a \"cloud free\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"gap-free\"''' and climatology data)\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_BLK_BGC_L4_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00304", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-multi-climatology-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-plankton-my-l4-olci-300m-p1m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-pp-my-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-my-009-154:cmems-obs-oc-blk-bgc-pp-my-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-plankton-nrt-l4-gapfree-multi-1km-p1d-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"abstract": "'''Short description:'''\n\nFor the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated '''gap-free''' Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"\"multi'''\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"\"gap-free\"\"''' data)\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_BLK_BGC_L4_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00302", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-plankton-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-plankton-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-pp-nrt-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-pp-nrt-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-transp-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:oceancolour-blk-bgc-l4-nrt-009-152:cmems-obs-oc-blk-bgc-transp-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-optics-my-l3-multi-4km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-plankton-my-l3-multi-4km-p1d-202411,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-plankton-my-l3-olci-300m-p1d-202211,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-plankton-my-l3-olci-4km-p1d-202411,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-reflectance-my-l3-multi-4km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D_202207": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''.\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00280", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-reflectance-my-l3-olci-4km-p1d-202207,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-transp-my-l3-multi-4km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-103:cmems-obs-oc-glo-bgc-transp-my-l3-olci-4km-p1d-202207,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-04-09", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-107:c3s-obs-oc-glo-bgc-plankton-my-l3-multi-4km-p1d-202303,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202303": {"abstract": "'''Short description:'''\n\nFor the '''Global''' Ocean '''Satellite Observations''', Brockmann Consult (BC) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''\"\"multi\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily''', '''monthly'''.\n* Spatial resolutions: '''4 km''' (multi).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find these products in the catalogue, use the search keyword '''\"\"ESA-CCI\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00282", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-my-009-107:c3s-obs-oc-glo-bgc-reflectance-my-l3-multi-4km-p1d-202303,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour Plankton and Reflectances MY L3 daily observations"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-optics-nrt-l3-multi-4km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-plankton-nrt-l3-multi-4km-p1d-202411,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-plankton-nrt-l3-olci-300m-p1d-202207,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-plankton-nrt-l3-olci-4km-p1d-202411,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-reflectance-nrt-l3-multi-4km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-reflectance-nrt-l3-olci-300m-p1d-202211,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-reflectance-nrt-l3-olci-4km-p1d-202207,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''daily'''\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00278", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-transp-nrt-l3-multi-4km-p1d-202311,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l3-nrt-009-101:cmems-obs-oc-glo-bgc-transp-nrt-l3-olci-4km-p1d-202207,global-ocean,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-optics-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and S3A & S3B only for the '''\"\"olci\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"\"cloud free\"\" product.\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"\"GlobColour\"\"'''.\"\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00281", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-gapfree-multi-4km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-multi-4km-p1m-202411,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-multi-climatology-4km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-olci-300m-p1m-202211,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-plankton-my-l4-olci-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-pp-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-reflectance-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-reflectance-my-l4-olci-300m-p1m-202211,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-reflectance-my-l4-olci-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-transp-my-l4-gapfree-multi-4km-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-transp-my-l4-multi-4km-p1m-202311,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "chl,coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-104:cmems-obs-oc-glo-bgc-transp-my-l4-olci-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,pft,primary-production-of-biomass-expressed-as-carbon,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_108:c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202207": {"abstract": "'''Short description:'''\n\nFor the '''Global''' Ocean '''Satellite Observations''', Brockmann Consult (BC) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''\"\"multi\"\"''' products.\n* Variables: Chlorophyll-a ('''CHL''').\n\n* Temporal resolutions: '''monthly'''.\n* Spatial resolutions: '''4 km''' (multi).\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find these products in the catalogue, use the search keyword '''\"\"ESA-CCI\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00283", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-my-009-108:c3s-obs-oc-glo-bgc-plankton-my-l4-multi-4km-p1m-202207,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour Plankton MY L4 monthly observations"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M_202311": {"abstract": "'''Short description: '''\n\nFor the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and S3A & S3B only for the '''\"olci\"''' products.\n* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').\n\n* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a \"cloud free\" product.\n* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.\n* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').\n\nTo find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''\"GlobColour\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00279", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-optics-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-gapfree-multi-4km-p1d-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-multi-4km-p1m-202411,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-olci-300m-p1m-202211,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-plankton-nrt-l4-olci-4km-p1m-202207,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-pp-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-reflectance-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-reflectance-nrt-l4-olci-300m-p1m-202211,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-reflectance-nrt-l4-olci-4km-p1m-202207,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-transp-nrt-l4-gapfree-multi-4km-p1d-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-transp-nrt-l4-multi-4km-p1m-202311,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-glo-bgc-l4-nrt-009-102:cmems-obs-oc-glo-bgc-transp-nrt-l4-olci-4km-p1m-202207,global-ocean,kd490,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\n*cmems_obs_oc_nws_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00107", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-ibi-bgc-hr-l3-nrt-009-204:cmems-obs-oc-ibi-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Iberic Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. \n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n*cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00108", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-ibi-bgc-hr-l4-nrt-009-210:cmems-obs-oc-ibi-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Iberic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\n*cmems_obs_oc_ibi_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_ibi_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00109", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-hr-l3-nrt-009-205:cmems-obs-oc-med-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1-) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n*cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1M-v01+D19\n*cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00110", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-hr-l4-nrt-009-211:cmems-obs-oc-med-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D_202311": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"multi'''\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n* '''''pp''''' with the Integrated Primary Production (PP)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_MED_BGC_L3_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00299", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-optics-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-plankton-my-l3-multi-1km-p1d-202411,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-plankton-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-reflectance-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-reflectance-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-transp-my-l3-multi-1km-p1d-202311,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-my-009-143:cmems-obs-oc-med-bgc-transp-my-l3-olci-300m-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-09-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-optics-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-plankton-nrt-l3-multi-1km-p1d-202211,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)\n* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"\"multi'''\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolution''': daily\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_MED_BGC_L3_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00297", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-plankton-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-reflectance-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-reflectance-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-transp-nrt-l3-multi-1km-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l3-nrt-009-141:cmems-obs-oc-med-bgc-transp-nrt-l3-olci-300m-p1d-202207,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-cryptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-phytoplankton,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-gapfree-multi-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-multi-1km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-multi-climatology-1km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M_202211": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated '''gap-free''' Chl concentration (to provide a \"cloud free\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided.\n* '''''pp''''' with the Integrated Primary Production (PP).\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"multi\"''' products, and OLCI-S3A & S3B for the '''\"olci\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"gap-free\"''' and climatology data)\n\n'''Spatial resolution''': 1 km for '''\"multi\"''' and 300 meters for '''\"olci\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"OCEANCOLOUR_MED_BGC_L4_MY\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00300", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-plankton-my-l4-olci-300m-p1m-202211,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-pp-my-l4-multi-4km-p1d-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-my-009-144:cmems-obs-oc-med-bgc-pp-my-l4-multi-4km-p1m-202311,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-plankton-nrt-l4-gapfree-multi-1km-p1d-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-plankton-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-plankton-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-pp-nrt-l4-multi-4km-p1d-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-pp-nrt-l4-multi-4km-p1m-202411,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"abstract": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-transp-nrt-l4-multi-1km-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"abstract": "'''Short description:'''\n\nFor the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR \u2013 Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:\n* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated '''gap-free''' Chl concentration (to provide a \"\"cloud free\"\" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)\n* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''\"\"multi'''\"\" observations achieved via region-specific algorithm, Volpe et al., 2019)\n\n'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''\"\"multi\"\"''' products, and OLCI-S3A & S3B for the '''\"\"olci\"\"''' products\n\n'''Temporal resolutions''': monthly and daily (for '''\"\"gap-free\"\"''' data)\n\n'''Spatial resolutions''': 1 km for '''\"\"multi\"\"''' and 300 meters for '''\"\"olci\"\"'''\n\nTo find this product in the catalogue, use the search keyword '''\"\"OCEANCOLOUR_MED_BGC_L4_NRT\"\"'''.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00298", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-med-bgc-l4-nrt-009-142:cmems-obs-oc-med-bgc-transp-nrt-l4-olci-300m-p1m-202207,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2023-04-10", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours up to 3 days after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel).\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n\n*cmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-v01\n*cmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00118", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-nws-bgc-hr-l3-nrt-009-203:cmems-obs-oc-nws-bgc-tur-spm-chl-nrt-l3-hr-mosaic-p1d-m-202107,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North West Shelf Region, Bio-Geo-Chemical, L3, daily observation"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"abstract": "'''Short description:'''\n\nThe High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in \u00b5g/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km\u00b2. BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azc\u00e1rate et al., 2021). These types of L4 products are generated and delivered one month after the respective period.\n\n'''Processing information:'''\n\nThe HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of:\n* Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone.\n* Application of a glint correction taking into account the detector viewing angles\n* Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression.\n* Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area.\n* invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection.\n* Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. \n* Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month.\n* Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day.\n\n'''Description of observation methods/instruments:'''\n\nOcean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton.\n\n'''Quality / Accuracy / Calibration information:'''\n\nA detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212.\n\n'''Suitability, Expected type of users / uses:'''\n\nThis product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies.\n\n'''Dataset names: '''\n*cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1M-v01\n*cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1D-v01\n*cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1D-v01\n\n'''Files format:'''\n*netCDF-4, CF-1.7\n*INSPIRE compliant.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00119", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:oceancolour-nws-bgc-hr-l4-nrt-009-209:cmems-obs-oc-nws-bgc-tur-spm-chl-nrt-l4-hr-mosaic-p1d-m-202107,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-suspended-matter-in-sea-water,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-water-turbidity,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North West Shelf Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-ssmi-merged-p30d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-ssmi-merged-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-p2d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-phy-my-drift-cfosat-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P2D_202311": {"abstract": "'''Short description:''' \n\nAntarctic sea ice displacement during winter from medium resolution sensors since 2002\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00120", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-physic-my-drift-amsr-p2d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311": {"abstract": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311", "instrument": null, "keywords": "antarctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-ant-phy-l3-my-011-018:cmems-obs-si-ant-physic-my-drift-amsr-p3d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2003-04-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Antarctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021:cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m_202211": {"abstract": "'''Short description:''' \nArctic Sea and Ice surface temperature\n\n'''Detailed description:''' \nArctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n'''DOI (product) :'''\nhttps://doi.org/10.48670/moi-00315", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-phy-climate-l3-my-011-021:cmems-obs-si-arc-phy-my-l3s-dmioi-p1d-m-202211,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-06-30", "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016:cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m_202105": {"abstract": "'''Short description:''' \nArctic Sea and Ice surface temperature\n\n'''Detailed description:'''\nArctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00123", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-phy-climate-l4-my-011-016:cmems-obs-si-arc-phy-my-l4-dmioi-p1d-m-202105,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-06-30", "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-30days-drift-ascat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-30days-drift-quickscat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-ascat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-ascat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-quickscat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-3days-drift-quickscat-ssmi-merged-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-6days-drift-ascat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"abstract": "'''Short description:''' \n\nArctic sea ice drift dataset at 3, 6 and 30 day lag during winter. The Arctic low resolution sea ice drift products provided from IFREMER have a 62.5 km grid resolution. They are delivered as daily products at 3, 6 and 30 days for the cold season extended at fall and spring: from September until May, it is updated on a monthly basis. The data are Merged product from radiometer and scatterometer :\n* SSM/I 85 GHz V & H Merged product (1992-1999)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00126", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cersat-glo-seaice-6days-drift-quickscat-ran-obs-full-time-serie-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-drift-my-l3-ssmi-p30d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-drift-my-l3-ssmi-p3d-202311,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-ssmi-merged-p30d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-ssmi-merged-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-p3d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411": {"abstract": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eastward-sea-ice-velocity,eo:mo:dat:seaice-arc-seaice-l3-rep-observations-011-010:cmems-obs-si-arc-phy-my-drift-cfosat-p6d-202411,level-3,marine-resources,marine-safety,multi-year,northward-sea-ice-velocity,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean Sea Ice Drift REPROCESSED"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008:DMI-ARC-SEAICE_TEMP-L4-NRT-OBS": {"abstract": "'''Short description:'''\n\nArctic Sea and Ice surface temperature product based upon observations from the Metop_A AVHRR instrument. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00130", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-arc-seaice-l4-nrt-observations-011-008:dmi-arc-seaice-temp-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-surface-temperature,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea and Ice Surface Temperature"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m_202211": {"abstract": "Gridded sea ice concentration, sea ice extent and classification based on the digitized Baltic ice charts produced by the FMI/SMHI ice analysts. It is produced daily in the afternoon, describing the ice situation daily at 14:00 EET. The nominal resolution is about 1km. The temporal coverage is from the beginning of the season 1980-1981 until today.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00131", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-phy-l4-my-011-019:cmems-obs-si-bal-phy-sit-my-l4-1km-p1d-m-202211,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-classification,sea-ice-concentration,sea-ice-extent,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2024-06-04", "missionStartDate": "1981-12-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea ice concentration, extent, and classification time series"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112": {"abstract": "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-phy-l4-my-011-019:cmems-obs-si-bal-seaice-conc-my-1km-202112,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-classification,sea-ice-concentration,sea-ice-extent,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2024-06-04", "missionStartDate": "1981-12-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea ice concentration, extent, and classification time series"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_CONC-L4-NRT-OBS": {"abstract": "'''Short description:''' \n\nFor the Baltic Sea- The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The parameters are based on ice chart produced on daily basis during the Baltic Sea ice season and show the ice concentration in a 1 km grid. Ice thickness chart (ITC) is a product based on the most recent available ice chart (IC) and a SAR image. The SAR data is used to update the ice information in the IC. The ice regions in the IC are updated according to a SAR segmentation and new ice thickness values are assigned to each SAR segment based on the SAR backscattering and the ice IC thickness range at that location.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00132", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-seaice-l4-nrt-observations-011-004:fmi-bal-seaice-conc-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-extent,sea-ice-thickness,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS": {"abstract": "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:seaice-bal-seaice-l4-nrt-observations-011-004:fmi-bal-seaice-thick-l4-nrt-obs,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-extent,sea-ice-thickness,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - Sea Ice Concentration and Thickness Charts"}, "EO:MO:DAT:SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013:c3s_obs-si_glo_phy_my_nh-l3_P1M_202411": {"abstract": "'''Short description:'''\n\nArctic sea ice L3 data in separate monthly files. The time series is based on reprocessed radar altimeter satellite data from Envisat and CryoSat and is available in the freezing season between October and April. The product is brokered from the Copernicus Climate Change Service (C3S).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00127", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-phy-climate-l3-my-011-013:c3s-obs-si-glo-phy-my-nh-l3-p1m-202411,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-thickness,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2015-04-01", "missionStartDate": "2002-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Arctic Ocean - Sea Ice Thickness REPROCESSED"}, "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411": {"abstract": "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411", "instrument": null, "keywords": "eo:mo:dat:seaice-glo-phy-l4-nrt-011-014:esa-obs-si-arc-phy-sit-nrt-l4-multi-p1d-m-202411", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": null}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_nh_P1D-m_202411": {"abstract": "'''Short description:''' \n\nFor the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers five global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401, OSI-402, OSI-403, OSI-405 and OSI-408). The sea ice concentration, edge and type products are delivered daily at 10km resolution and the sea ice drift in 62.5km resolution, all in polar stereographic projections covering the Northern Hemisphere and the Southern Hemisphere. The sea ice drift motion vectors have a time-span of 2 days. These are the Sea Ice operational nominal products for the Global Ocean.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00134", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-nrt-observations-011-001:osisaf-obs-si-glo-phy-sidrift-nrt-nh-p1d-m-202411,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,sea-ice-x-displacement,sea-ice-y-displacement,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411": {"abstract": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-nrt-observations-011-001:osisaf-obs-si-glo-phy-sidrift-nrt-sh-p1d-m-202411,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-ice-classification,sea-ice-x-displacement,sea-ice-y-displacement,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-05-04", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS_202003": {"abstract": "'''Short description:''' \nThe CDR and ICDR sea ice concentration dataset of the EUMETSAT OSI SAF (OSI-450-a and OSI-430-a), covering the period from October 1978 to present, with 16 days delay. It used passive microwave data from SMMR, SSM/I and SSMIS. Sea ice concentration is computed from atmospherically corrected PMW brightness temperatures, using a combination of state-of-the-art algorithms and dynamic tie points. It includes error bars for each grid cell (uncertainties). This version 3.0 of the CDR (OSI-450-a, 1978-2020) and ICDR (OSI-430-a, 2021-present with 16 days latency) was released in November 2022\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00136", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-rep-observations-011-009:osisaf-glo-seaice-conc-cont-timeseries-nh-la-obs-202003,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1979-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003": {"abstract": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003", "instrument": null, "keywords": "antarctic-ocean,arctic-ocean,coastal-marine-environment,eo:mo:dat:seaice-glo-seaice-l4-rep-observations-011-009:osisaf-glo-seaice-conc-cont-timeseries-sh-la-obs-202003,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1979-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF)"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_al-l3-duacs_PT1S_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n\u201c\u2019Associated products\u201d\u2019\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00139", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-al-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_alg-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_alg-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-alg-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-c2-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-c2n-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-e1-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1g-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1g-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-e1g-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-e2-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_en-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_en-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-en-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_enn-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_enn-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-enn-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_g2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_g2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-g2-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2a-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2a-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-h2a-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2ag-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2ag-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-h2ag-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2b-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2b-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-h2b-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j1-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1g-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1g-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j1g-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j1n-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j2-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2g-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2g-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j2g-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j2n-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j3-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-j3n-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3a-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3a-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-s3a-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3b-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3b-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-s3b-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-s6a-lr-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swon-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swon-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-swon-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swonc-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swonc-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-swonc-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tp-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tp-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-tp-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tpn-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tpn-l3-duacs_PT1S_202411", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l3-my-008-061:cmems-obs-sl-eur-phy-ssh-my-tpn-l3-duacs-pt1s-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1D_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00141", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-my-008-068:cmems-obs-sl-eur-phy-ssh-my-allsat-l4-duacs-0.0625deg-p1d-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411": {"abstract": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-my-008-068:cmems-obs-sl-eur-phy-ssh-my-allsat-l4-duacs-0.0625deg-p1m-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.0625deg_P1D_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00142", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-nrt-008-060:cmems-obs-sl-eur-phy-ssh-nrt-allsat-l4-duacs-0.0625deg-p1d-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": [], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202311": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00142", "instrument": null, "keywords": "baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:sealevel-eur-phy-l4-nrt-008-060:cmems-obs-sl-eur-phy-ssh-nrt-allsat-l4-duacs-0.125deg-p1d-202311,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D_202411": {"abstract": "'''Short description:''' \n\nDUACS delayed-time altimeter gridded maps of sea surface heights and derived variables over the global Ocean (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global?tab=overview). The processing focuses on the stability and homogeneity of the sea level record (based on a stable two-satellite constellation) and the product is dedicated to the monitoring of the sea level long-term evolution for climate applications and the analysis of Ocean/Climate indicators. These products are produced and distributed by the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/).\n\n'''DOI (product):'''\nhttps://doi.org/10.48670/moi-00145", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-climate-l4-my-008-057:c3s-obs-sl-glo-phy-ssh-my-twosat-l4-duacs-0.25deg-p1d-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-climate-l4-my-008-057:c3s-obs-sl-glo-phy-ssh-my-twosat-l4-duacs-0.25deg-p1m-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_al-l3-duacs_PT1S_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc.). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). \nThe product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)\n\n'''Associated products'''\nA time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.\n\n'''DOI (product)''':\nhttps://doi.org/10.48670/moi-00146", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-al-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_alg-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_alg-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-alg-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-c2-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-c2n-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-e1-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1g-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1g-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-e1g-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-e2-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_en-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_en-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-en-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_enn-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_enn-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-enn-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_g2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_g2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-g2-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2a-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2a-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-h2a-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2ag-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2ag-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-h2ag-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2b-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2b-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-h2b-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j1-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j1n-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j2-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2g-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2g-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j2g-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j2n-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j3-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3n-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3n-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-j3n-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3a-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3a-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-s3a-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3b-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3b-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-s3b-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-s6a-lr-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swon-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swon-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-swon-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swonc-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swonc-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-swonc-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tp-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tp-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-tp-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tpn-l3-duacs_PT1S_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tpn-l3-duacs_PT1S_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l3-my-008-062:cmems-obs-sl-glo-phy-ssh-my-tpn-l3-duacs-pt1s-202411,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1992-10-03", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications.\nThis product is processed by the DUACS multimission altimeter data processing system.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00148", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-my-008-047:cmems-obs-sl-glo-phy-ssh-my-allsat-l4-duacs-0.125deg-p1d-202411,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411": {"abstract": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-my-008-047:cmems-obs-sl-glo-phy-ssh-my-allsat-l4-duacs-0.125deg-p1m-m-202411,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2020-12-31", "missionStartDate": "1993-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING)"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202411": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00149", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-nrt-008-046:cmems-obs-sl-glo-phy-ssh-nrt-allsat-l4-duacs-0.125deg-p1d-202411,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": [], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D_202311": {"abstract": "'''Short description:'''\n\nAltimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications.\nThis product is processed by the DUACS multimission altimeter data processing system. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00149", "instrument": null, "keywords": "arctic-ocean,coastal-marine-environment,eo:mo:dat:sealevel-glo-phy-l4-nrt-008-046:cmems-obs-sl-glo-phy-ssh-nrt-allsat-l4-duacs-0.25deg-p1d-202311,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-height-above-geoid,sea-surface-height-above-sea-level,surface-geostrophic-eastward-sea-water-velocity,surface-geostrophic-eastward-sea-water-velocity-assuming-sea-level-for-geoid,surface-geostrophic-northward-sea-water-velocity,surface-geostrophic-northward-sea-water-velocity-assuming-sea-level-for-geoid,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT"}, "EO:MO:DAT:SST_ATL_PHY_L3S_MY_010_038:cmems_obs-sst_atl_phy_my_l3s_P1D-m_202411": {"abstract": "'''Short description:'''\n\nFor the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-my-010-038:cmems-obs-sst-atl-phy-my-l3s-p1d-m-202411,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Reprocessed"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_gir_P1D-m_202311": {"abstract": "'''Short description:'''\n\nFor the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02\u00b0 resolution grid. It includes observations by polar orbiting and geostationary satellites . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. 3 more datasets are available that only contain \"per sensor type\" data : Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00310", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-l3s-gir-p1d-m-202311,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-l3s-pir-p1d-m-202311,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-l3s-pmw-p1d-m-202311,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211": {"abstract": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-phy-l3s-nrt-010-037:cmems-obs-sst-atl-phy-nrt-l3s-p1d-m-202211,iberian-biscay-irish-seas,level-3,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025:IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE_201904": {"abstract": "'''Short description:'''\n\nFor the Atlantic European North West Shelf Ocean-European North West Shelf/Iberia Biscay Irish Seas. The ODYSSEA NW+IBI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg x 0.02deg horizontal resolution, using satellite data from both infra-red and micro-wave radiometers. It is the sea surface temperature operational nominal product for the Northwest Shelf Sea and Iberia Biscay Irish Seas.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00152", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-sst-l4-nrt-observations-010-025:ifremer-atl-sst-l4-nrt-obs-full-time-serie-201904,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2018-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas \u2013 High Resolution ODYSSEA L4 Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_ATL_SST_L4_REP_OBSERVATIONS_010_026:cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE_202411": {"abstract": "'''Short description:''' \n\nFor the European North West Shelf Ocean Iberia Biscay Irish Seas. The IFREMER Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.05deg. x 0.05deg. horizontal resolution, over the 1982-present period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-present). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00153", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-atl-sst-l4-rep-observations-010-026:cmems-ifremer-atl-sst-l4-rep-obs-full-time-serie-202411,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "European North West Shelf/Iberia Biscay Irish Seas - High Resolution L4 Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BAL_PHY_L3S_MY_010_040:cmems_obs-sst_bal_phy_my_l3s_P1D-m_202211": {"abstract": "'''Short description:''' \nFor the Baltic Sea- the DMI Sea Surface Temperature reprocessed L3S aims at providing daily multi-sensor supercollated data at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00312", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-phy-l3s-my-010-040:cmems-obs-sst-bal-phy-my-l3s-p1d-m-202211,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - L3S Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BAL_PHY_SUBSKIN_L4_NRT_010_034:cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m_202211": {"abstract": "'''Short description:'''\nFor the Baltic Sea - the DMI Sea Surface Temperature Diurnal Subskin L4 aims at providing hourly analysis of the diurnal subskin signal at 0.02deg. x 0.02deg. horizontal resolution, using the BAL L4 NRT product as foundation temperature and satellite data from infra-red radiometers. Uses SST satellite products from the sensors: Metop B AVHRR, Sentinel-3 A/B SLSTR, VIIRS SUOMI NPP & NOAA20 \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00309", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-phy-subskin-l4-nrt-010-034:cmems-obs-sst-bal-phy-subskin-nrt-l4-pt1h-m-202211,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2022-05-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032:DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE_201904": {"abstract": "'''Short description:''' \n\nFor the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00154", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-sst-l3s-nrt-observations-010-032:dmi-baltic-sst-l3s-nrt-obs-full-time-serie-201904,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "North Sea/Baltic Sea - Sea Surface Temperature Analysis L3S"}, "EO:MO:DAT:SST_BAL_SST_L4_REP_OBSERVATIONS_010_016:DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016_202012": {"abstract": "'''Short description:''' \nFor the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. The product uses SST satellite products from the ESA CCI and Copernicus C3S projects, including the sensors: NOAA AVHRRs 7, 9, 11, 12, 14, 15, 16, 17, 18 , 19, Metop, ATSR1, ATSR2, AATSR and SLSTR.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00156", "instrument": null, "keywords": "baltic-sea,coastal-marine-environment,eo:mo:dat:sst-bal-sst-l4-rep-observations-010-016:dmi-bal-sst-l4-rep-observations-010-016-202012,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Baltic Sea- Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BS_PHY_L3S_MY_010_041:cmems_obs-sst_bs_phy_my_l3s_P1D-m_202411": {"abstract": "'''Short description:''' \n\nThe Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from January 1st 1982 to present (currently, up to one month before real time). The BS-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives, but also includes in input an adjusted version of the AVHRR Pathfinder dataset version 5.3 to increase the input observation coverage. Due to Brexit, an interim production guarantees the temporal extension of the BS-REP-L3S product since 1st January 2023 to present.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00313", "instrument": null, "keywords": "adjusted-sea-surface-temperature,black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-phy-l3s-my-010-041:cmems-obs-sst-bs-phy-my-l3s-p1d-m-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1981-08-25", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_BS_PHY_SUBSKIN_L4_NRT_010_035:cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"abstract": "'''Short description:'''\n\nFor the Black Sea - the CNR diurnal sub-skin Sea Surface Temperature product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Black Sea (BS) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS BS Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00157", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-phy-subskin-l4-nrt-010-035:cmems-obs-sst-blk-phy-sst-nrt-diurnal-oi-0.0625deg-pt1h-m-202105,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2020-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a_202311": {"abstract": "'''Short description:''' \n\nFor the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Black Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00158", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l3s-nrt-observations-010-013:sst-bs-sst-l3s-nrt-observations-010-013-a-202311,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311": {"abstract": "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l3s-nrt-observations-010-013:sst-bs-sst-l3s-nrt-observations-010-013-b-202311,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b": {"abstract": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-ssta-l4-nrt-observations-010-006-b,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d": {"abstract": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-ssta-l4-nrt-observations-010-006-d,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2_202311": {"abstract": "'''Short description:''' \n\nFor the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain providess daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Black Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00159", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-sst-l4-nrt-observations-010-006-a-v2-202311,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311": {"abstract": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-nrt-observations-010-006:sst-bs-sst-l4-nrt-observations-010-006-c-v2-202311,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_BS_SST_L4_REP_OBSERVATIONS_010_022:cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022_202411": {"abstract": "'''Short description:''' \n\nThe Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from January 1st 1982 to present (currently, up to one month before real time). The BS-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives, but also includes in input an adjusted version of the AVHRR Pathfinder dataset version 5.3 to increase the input observation coverage. Due to Brexit, an interim production guarantees the temporal extension of the BS-REP-L4 product since 1st January 2023 to present.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00160", "instrument": null, "keywords": "black-sea,coastal-marine-environment,eo:mo:dat:sst-bs-sst-l4-rep-observations-010-022:cmems-sst-bs-sst-l4-rep-observations-010-022-202411,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1981-08-25", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Black Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_GLO_PHY_L3S_MY_010_039:cmems_obs-sst_glo_phy_my_l3s_P1D-m_202311": {"abstract": "'''Short description:''' \n\nFor the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05\u00b0 resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. \n\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00329", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-phy-l3s-my-010-039:cmems-obs-sst-glo-phy-my-l3s-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_PHY_L4_NRT_010_043:cmems_obs-sst_glo_phy_nrt_l4_P1D-m_202303": {"abstract": "This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours.\n\nWhereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed.\n\nThe processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/mds-00321", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-phy-l4-nrt-010-043:cmems-obs-sst-glo-phy-nrt-l4-p1d-m-202303,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Sea Surface Temperature Gridded Level 4 Daily Multi-Sensor Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE_202211": {"abstract": "'''Short description:'''\n\nFor the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.1\u00b0 resolution global grid. It includes observations by polar orbiting (NOAA-18 & NOAAA-19/AVHRR, METOP-A/AVHRR, ENVISAT/AATSR, AQUA/AMSRE, TRMM/TMI) and geostationary (MSG/SEVIRI, GOES-11) satellites . The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.3 more datasets are available that only contain \"per sensor type\" data : Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00164", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:ifremer-glob-sst-l3-nrt-obs-full-time-serie-202211,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:cmems-obs-sst-glo-phy-l3s-gir-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:cmems-obs-sst-glo-phy-l3s-pir-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311": {"abstract": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l3s-nrt-observations-010-010:cmems-obs-sst-glo-phy-l3s-pmw-p1d-m-202311,global-ocean,level-3,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ODYSSEA Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations"}, "EO:MO:DAT:SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2": {"abstract": "'''Short description:''' \n\nFor the Global Ocean- the OSTIA global foundation Sea Surface Temperature product provides daily gap-free maps of : Foundation Sea Surface Temperature at 0.05\u00b0 x 0.05\u00b0 horizontal grid resolution, using in-situ and satellite data from both infrared and microwave radiometers. \n\nThe Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the UK's Met Office and delivered by IFREMER PU. OSTIA uses satellite data provided by the GHRSST project together with in-situ observations to determine the sea surface temperature.\nA high resolution (1/20\u00b0 - approx. 6 km) daily analysis of sea surface temperature (SST) is produced for the global ocean and some lakes.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00165", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-glo-sst-l4-nrt-observations-010-001:metoffice-glo-sst-l4-nrt-obs-sst-v2,global-ocean,level-4,marine-resources,marine-safety,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,target-application#seaiceforecastingapplication,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2007-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:METOFFICE-GLO-SST-L4-REP-OBS-SST_202003": {"abstract": "'''Short description :'''\n\nThe OSTIA (Good et al., 2020) global sea surface temperature reprocessed product provides daily gap-free maps of foundation sea surface temperature and ice concentration (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal grid resolution, using in-situ and satellite data. This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00168", "instrument": null, "keywords": "/physical-oceanography/water-column-temperature-and-salinity,atlantic-ocean,canary-current-system,coastal-marine-environment,data,drivers-and-tipping-points,eo:mo:dat:sst-glo-sst-l4-rep-observations-010-011:metoffice-glo-sst-l4-rep-obs-sst-202003,global-ocean,level-4,marine-resources,marine-safety,modelling-data,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-surface-temperature,south-brazilian-shelf,south-mid-atlantic-ridge,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting,wp5-assessing-state", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-05-31", "missionStartDate": "1981-10-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:C3S-GLO-SST-L4-REP-OBS-SST_202211": {"abstract": "'''Short description:''' \nThe ESA SST CCI and C3S global Sea Surface Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the (A)ATSRs, SLSTR and the AVHRR series of sensors (Merchant et al., 2019). The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019).\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00169", "instrument": null, "keywords": "analysed-sst-uncertainty,coastal-marine-environment,eo:mo:dat:sst-glo-sst-l4-rep-observations-010-024:c3s-glo-sst-l4-rep-obs-sst-202211,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-water-temperature,sea-water-temperature-standard-error,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-31", "missionStartDate": "1981-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211": {"abstract": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211", "instrument": null, "keywords": "analysed-sst-uncertainty,coastal-marine-environment,eo:mo:dat:sst-glo-sst-l4-rep-observations-010-024:esacci-glo-sst-l4-rep-obs-sst-202211,global-ocean,level-4,marine-resources,marine-safety,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-ice-area-fraction,sea-water-temperature,sea-water-temperature-standard-error,target-application#seaiceclimate,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": "2022-10-31", "missionStartDate": "1981-09-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "ESA SST CCI and C3S reprocessed sea surface temperature analyses"}, "EO:MO:DAT:SST_MED_PHY_L3S_MY_010_042:cmems_obs-sst_med_phy_my_l3s_P1D-m_202411": {"abstract": "'''Short description:''' \n\nThe Reprocessed (REP) Mediterranean Sea (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from January 1st 1982 to present (currently, up to one month before real time). The MED-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives, but also includes in input an adjusted version of the AVHRR Pathfinder dataset version 5.3 to increase the input observation coverage. Due to Brexit, an interim production guarantees the temporal extension of the MED-REP-L3S product since 1st January 2023 to present.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00314", "instrument": null, "keywords": "adjusted-sea-surface-temperature,coastal-marine-environment,eo:mo:dat:sst-med-phy-l3s-my-010-042:cmems-obs-sst-med-phy-my-l3s-p1d-m-202411,level-3,marine-resources,marine-safety,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1981-08-25", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution L3S Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:SST_MED_PHY_SUBSKIN_L4_NRT_010_036:cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"abstract": "''' Short description: ''' \n\nFor the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16\u00b0 (0.0625\u00b0) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).\n \n[https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-reference-copernicus-marine-products-and-services How to cite]\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00170", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-phy-subskin-l4-nrt-010-036:cmems-obs-sst-med-phy-sst-nrt-diurnal-oi-0.0625deg-pt1h-m-202105,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-subskin-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311": {"abstract": "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l3s-nrt-observations-010-012:sst-med-sst-l3s-nrt-observations-010-012-a-202311,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b_202311": {"abstract": "'''Short description:''' \n\nFor the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Mediterranean Sea at high (1/16\u00b0) and Ultra High (0.01\u00b0) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The main L2P data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00171", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l3s-nrt-observations-010-012:sst-med-sst-l3s-nrt-observations-010-012-b-202311,level-3,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-foundation-temperature,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b": {"abstract": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-ssta-l4-nrt-observations-010-004-b,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d": {"abstract": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-ssta-l4-nrt-observations-010-004-d,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311": {"abstract": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-sst-l4-nrt-observations-010-004-a-v2-202311,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2_202311": {"abstract": "'''Short description:''' \n\nFor the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides daily gap-free (L4) maps at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution over the Mediterranean Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The main upstream data currently used include SLSTR-3A/3B, VIIRS-N20/NPP, Metop-B/C AVHRR and SEVIRI. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625\u00b0) and ultra-high (UHR 0.01\u00b0) spatial resolution, applying statistical techniques. These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00172", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-nrt-observations-010-004:sst-med-sst-l4-nrt-observations-010-004-c-v2-202311,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2008-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis"}, "EO:MO:DAT:SST_MED_SST_L4_REP_OBSERVATIONS_010_021:cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021_202411": {"abstract": "'''Short description:''' \n \nThe Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05\u00b0 resolution grid covering the period from January 1st 1982 to present (currently, up to one month before real time). The MED-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives, but also includes in input an adjusted version of the AVHRR Pathfinder dataset version 5.3 to increase the input observation coverage. Due to Brexit, an interim production guarantees the temporal extension of the MED-REP-L4 product since 1st January 2023 to present.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00173", "instrument": null, "keywords": "coastal-marine-environment,eo:mo:dat:sst-med-sst-l4-rep-observations-010-021:cmems-sst-med-sst-l4-rep-observations-010-021-202411,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1982-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Mediterranean Sea - High Resolution L4 Sea Surface Temperature Reprocessed"}, "EO:MO:DAT:WAVE_GLO_PHY_SPC_L4_NRT_014_004:cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H_202112": {"abstract": "'''Short description:'''\n\nNear-Real-Time multi-mission global satellite-based spectral integral parameters. Only valid data are used, based on the L3 corresponding product. Included wave parameters are partition significant wave height, partition peak period and partition peak or principal direction. Those parameters are propagated in space and time at a 3-hour timestep and on a regular space grid, providing information of the swell propagation characteristics, from source to land. One file gathers one swell system, gathering observations originating from the same storm source. This product is processed by the WAVE-TAC multi-mission SAR data processing system to serve in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B. All the spectral parameter measurements are optimally interpolated using swell observations belonging to the same swell field. The SAR data processing system produces wave integral parameters by partition (partition significant wave height, partition peak period and partition peak or principal direction) and the associated standard deviation and density of propagated observations. \n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00175", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-spc-l4-nrt-014-004:cmems-obs-wave-glo-phy-spc-nrt-multi-l4-1deg-pt3h-202112,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2021-11-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-0.5deg_P1D-i_202411": {"abstract": "'''Short description:'''\n\nMulti-Year gridded multi-mission merged satellite significant wave height. Only valid data are included. This Multi-Year product is processed by the WAVE-TAC multi-mission altimeter data processing system and is based on CMEMS Multi-Year level-3 SWH datasets (see the product WAVE_GLO_PHY_SWH_L3_MY_014_005).\nIt merges along-track SWH data from the following missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa, Jason-3 and CFOSAT. The resulting gridded product has a 2\u00b0 horizontal resolution and is produced daily. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00177", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-my-014-007:cmems-obs-wave-glo-phy-swh-my-multi-l4-0.5deg-p1d-i-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,sea-surface-wave-significant-height-daily-maximum,sea-surface-wave-significant-height-daily-mean,sea-surface-wave-significant-height-daily-number-of-observations,sea-surface-wave-significant-height-daily-standard-deviation,sea-surface-wave-significant-height-mapping-score,sea-surface-wave-significant-height-number-of-observations,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2002-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411": {"abstract": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-my-014-007:cmems-obs-wave-glo-phy-swh-my-multi-l4-2deg-p1d-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,sea-surface-wave-significant-height-daily-maximum,sea-surface-wave-significant-height-daily-mean,sea-surface-wave-significant-height-daily-number-of-observations,sea-surface-wave-significant-height-daily-standard-deviation,sea-surface-wave-significant-height-mapping-score,sea-surface-wave-significant-height-number-of-observations,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2002-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411": {"abstract": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-nrt-014-003:cmems-obs-wave-glo-phy-swh-nrt-multi-l4-2deg-p1d-i-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-06-26", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411": {"abstract": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-nrt-014-003:cmems-obs-wave-glo-phy-swh-nrt-multi-l4-2deg-p1d-m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-06-26", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D_202211": {"abstract": "'''Short description:'''\n\nNear-Real-Time gridded multi-mission merged satellite significant wave height. Only valid data are included. This product is processed in Near-Real-Time by the WAVE-TAC multi-mission altimeter data processing system and is based on CMEMS level-3 SWH datasets (see the product WAVE_GLO_WAV_L3_SWH_NRT_OBSERVATIONS_014_001).\nIt merges along-track SWH data from the following missions: Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT and HaiYang-2B. The resulting gridded product has a 2\u00b0 horizontal resolution and is produced daily. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00180", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eo:mo:dat:wave-glo-phy-swh-l4-nrt-014-003:cmems-obs-wave-glo-phy-swh-nrt-multi-l4-2deg-p1d-202211,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,not-applicable,oceanographic-geographical-features,satellite-observation,sea-surface-wave-significant-height,weather-climate-and-seasonal-forecasting", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2019-06-26", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"}, "EO:MO:DAT:WIND_GLO_PHY_CLIMATE_L4_MY_012_003:cmems_obs-wind_glo_phy_my_l4_P1M_202411": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT, QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT satellite instruments. The product provides monthly mean stress-equivalent wind and stress variables as well as their standard deviation. The number of observations used to calculate the monthly averages are included in the product.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00181", "instrument": null, "keywords": "arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-climate-l4-my-012-003:cmems-obs-wind-glo-phy-my-l4-p1m-202411,global-ocean,iberian-biscay-irish-seas,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1992-05-16", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers1-scat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers1-scat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers2-scat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-ers2-scat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-asc-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-des-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopa-ascat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-asc-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-des-0.125deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"abstract": "'''Short description:''' \n\nFor the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n*0.5 degrees grid for the 50 km scatterometer L2 inputs,\n*0.25 degrees grid based on 25 km scatterometer swath observations,\n*and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. \n\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The MY L3 products follow the availability of the reprocessed EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees;\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00183", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-metopb-ascat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-asc-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-oceansat2-oscat-des-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-asc-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-asc-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-des-0.25deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-my-012-005:cmems-obs-wind-glo-phy-my-l3-quikscat-seawinds-des-0.5deg-p1d-i-202311,global-ocean,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1991-08-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i_202311": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products:\n\n*0.5 degrees grid for the 50 km scatterometer L2 inputs,\n*0.25 degrees grid based on 25 km scatterometer swath observations,\n*and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products.\n\nData from ascending and descending passes are gridded separately.\nThe product provides stress-equivalent wind and stress variables as well as their divergence and curl. The NRT L3 products follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for:\n*The ASCAT scatterometers on Metop-A (discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees;\n*The OSCAT scatterometer on Scatsat-1 (discontinued on 28/02/2021) and Oceansat-3 at 0.25 and 0.5 degrees; \n*The HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees\n\nIn addition, the product includes European Centre for Medium-Range Weather Forecasts (ECMWF) operational model forecast wind and stress variables collocated with the scatterometer observations at L2 and processed to L3 in exactly the same way as the scatterometer observations.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00182", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2b-hscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2c-hscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-hy2d-hscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-asc-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-des-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopa-ascat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-asc-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-des-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopb-ascat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-asc-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-des-0.125deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-metopc-ascat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-asc-0.25deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-asc-0.5deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-des-0.25deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-oceansat3-oscat-des-0.5deg-p1d-i-202406,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-asc-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-asc-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-des-0.25deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311", "instrument": null, "keywords": "air-density,arctic-ocean,baltic-sea,black-sea,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l3-nrt-012-002:cmems-obs-wind-glo-phy-nrt-l3-scatsat1-oscat-des-0.5deg-p1d-i-202311,global-ocean,iberian-biscay-irish-seas,level-3,magnitude-of-surface-downward-stress,marine-resources,marine-safety,mediterranean-sea,near-real-time,north-west-shelf-seas,northward-wind,not-applicable,oceanographic-geographical-features,satellite-observation,status-flag,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-speed,wind-to-direction,wvc-index,wvc-index-eastward-wind", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "2016-01-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Daily Gridded Sea Surface Winds from Scatterometer"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211": {"abstract": "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l4-my-012-006:cmems-obs-wind-glo-phy-my-l4-0.125deg-pt1h-202211,global-ocean,level-4,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-06-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H_202406": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 and 0.25 degrees horizontal spatial resolution. Scatterometer observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF ERA5 model fields. Bias corrections are based on scatterometer observations from Metop-A, Metop-B, Metop-C ASCAT (0.125 degrees) and QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT (0.25 degrees). The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00185", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l4-my-012-006:cmems-obs-wind-glo-phy-my-l4-0.25deg-pt1h-202406,global-ocean,level-4,marine-resources,marine-safety,multi-year,northward-wind,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": "1997-06-01", "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model"}, "EO:MO:DAT:WIND_GLO_PHY_L4_NRT_012_004:cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H_202207": {"abstract": "'''Short description:'''\n\nFor the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product.\n\n'''DOI (product) :''' \nhttps://doi.org/10.48670/moi-00305", "instrument": null, "keywords": "air-density,coastal-marine-environment,eastward-wind,eo:mo:dat:wind-glo-phy-l4-nrt-012-004:cmems-obs-wind-glo-phy-nrt-l4-0.125deg-pt1h-202207,global-ocean,level-4,marine-resources,marine-safety,near-real-time,northward-wind,not-applicable,numerical-model,oceanographic-geographical-features,satellite-observation,stress-curl,stress-divergence,surface-downward-eastward-stress,surface-downward-northward-stress,weather-climate-and-seasonal-forecasting,wind-curl,wind-divergence", "license": ["Copernicus_Marine_Service_Product_License"], "missionEndDate": null, "missionStartDate": null, "platform": null, "platformSerialIdentifier": null, "processingLevel": null, "title": "Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model"}}, "providers_config": {"EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1D-m_202105": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1D-m_202105"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_BGC_002_004:cmems_mod_arc_bgc_anfc_ecosmo_P1M-m_202211"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1D-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1D-m_202311"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_002_001:cmems_mod_arc_phy_anfc_6km_detided_P1M-m_202311"}, "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011:cmems_mod_arc_phy_anfc_nextsim_P1M-m_202311": {"collection": "EO:MO:DAT:ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011:cmems_mod_arc_phy_anfc_nextsim_P1M-m_202311"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1D-m_202105": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1D-m_202105"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1M_202105"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_BGC_002_005:cmems_mod_arc_bgc_my_ecosmo_P1Y_202211"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1D-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_hflux_P1M-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1D-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_mflux_P1M-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1D-m_202211"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1M_202012": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1M_202012"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_002_003:cmems_mod_arc_phy_my_topaz4_P1Y_202211"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1D-m_202411"}, "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411": {"collection": "EO:MO:DAT:ARCTIC_MULTIYEAR_PHY_ICE_002_016:cmems_mod_arc_phy_my_nextsim_P1M-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_7-10days_P1D-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_7-10days_P1D-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc-pp_anfc_P1D-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_BGC_003_007:cmems_mod_bal_bgc_anfc_P1M-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided-7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-cur_anfc_detided_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided-7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy-ssh_anfc_detided_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT15M-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_7-10days_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1D-m_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1M-m_202311": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_P1M-m_202311"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT15M-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_PHY_003_006:cmems_mod_bal_phy_anfc_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_7-10days_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_PT1H-i_202311": {"collection": "EO:MO:DAT:BALTICSEA_ANALYSISFORECAST_WAV_003_010:cmems_mod_bal_wav_anfc_PT1H-i_202311"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1D-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1M-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1Y-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_BGC_003_012:cmems_mod_bal_bgc_my_P1Y-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1D-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1D-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1M-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_PHY_003_011:cmems_mod_bal_phy_my_P1Y-m_202303"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_2km-climatology_P1M-m_202411"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_PT1H-i_202411"}, "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411": {"collection": "EO:MO:DAT:BALTICSEA_MULTIYEAR_WAV_003_015:cmems_mod_bal_wav_my_aflux_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-bio_anfc_3km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-car_anfc_3km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_PT1H-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-co2_anfc_3km_PT1H-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-nut_anfc_3km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-opt_anfc_3km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-optics_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pft_anfc_3km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_BGC_007_010:cmems_mod_blk_bgc-pp-o2_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT15M-i_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided-2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_detided_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-cur_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-mld_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-sal_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT15M-i_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided-2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_detided_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-ssh_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_PT1H-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_2.5km_PT1H-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-tem_anfc_mrm-500m_PT1H-i_202311"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_PHY_007_001:cmems_mod_blk_phy-temp_anfc_2.5km_PT1H-m_202411"}, "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_WAV_007_003:cmems_mod_blk_wav_anfc_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_ANALYSISFORECAST_WAV_007_003:cmems_mod_blk_wav_anfc_2.5km_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-bio_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-car_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-co2_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-nut_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1D-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_P1Y-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_my_2.5km_climatology_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_BGC_007_005:cmems_mod_blk_bgc-plankton_myint_2.5km_P1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-cur_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-hflux_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mflux_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-mld_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-sal_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-ssh_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km-climatology_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_my_2.5km_P1Y-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-temp_myint_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1D-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_PHY_007_004:cmems_mod_blk_phy-wflux_my_2.5km_P1M-m_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav-aflux_my_2.5km_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m_202311": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km-climatology_PT1M-m_202311"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_my_2.5km_PT1H-i_202411"}, "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411": {"collection": "EO:MO:DAT:BLKSEA_MULTIYEAR_WAV_007_006:cmems_mod_blk_wav_myint_2.5km_PT1H-i_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-bio_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-car_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-co2_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-nut_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-optics_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-pft_anfc_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1D-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_BGC_001_028:cmems_mod_glo_bgc-plankton_anfc_0.25deg_P1M-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-cur_anfc_0.083deg_PT6H-i_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-so_anfc_0.083deg_PT6H-i_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-thetao_anfc_0.083deg_PT6H-i_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy-wcur_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-climatology-uncertainty_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1D-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg-sst-anomaly_P1M-m_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_0.083deg_PT1H-m_202406"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-sl_PT1H-i_202411"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_PHY_001_024:cmems_mod_glo_phy_anfc_merged-uv_PT1H-i_202211"}, "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_WAV_001_027:cmems_mod_glo_wav_anfc_0.083deg_PT3H-i_202411": {"collection": "EO:MO:DAT:GLOBAL_ANALYSISFORECAST_WAV_001_027:cmems_mod_glo_wav_anfc_0.083deg_PT3H-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_my_0.25deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1D-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1D-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_029:cmems_mod_glo_bgc_myint_0.25deg_P1M-m_202406"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i_202411": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl-Fphy_PT1D-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_BGC_001_033:cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg-climatology_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_my_0.083deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_001_030:cmems_mod_glo_phy_myint_0.083deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-all_my_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1D-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_PHY_ENS_001_031:cmems_mod_glo_phy-mnstd_my_0.25deg_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg-climatology_P1M-m_202311"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_my_0.2deg_PT3H-i_202411"}, "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_myint_0.2deg_PT3H-i_202311": {"collection": "EO:MO:DAT:GLOBAL_MULTIYEAR_WAV_001_032:cmems_mod_glo_wav_myint_0.2deg_PT3H-i_202311"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc-optics_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_BGC_005_004:cmems_mod_ibi_bgc_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-cur_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-ssh_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy-wcur_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT15M-i_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-2D_PT1H-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_PHY_005_001:cmems_mod_ibi_phy_anfc_0.027deg-3D_PT1H-m_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.027deg_PT1H-i_202411"}, "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.05deg_PT1H-i_202311": {"collection": "EO:MO:DAT:IBI_ANALYSISFORECAST_WAV_005_005:cmems_mod_ibi_wav_anfc_0.05deg_PT1H-i_202311"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc-plankton_my_0.083deg_P1Y-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D-climatology_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1D-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1M-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m_202211": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_BGC_005_003:cmems_mod_ibi_bgc_my_0.083deg-3D_P1Y-m_202211"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-hflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-mflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wcur_0.083deg_P1Y-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1D-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my-wflux_0.083deg_P1M-m_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-2D_PT1H-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D-climatology_P1M-m_202211"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1D-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1M-m_202012"}, "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m_202211": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_PHY_005_002:cmems_mod_ibi_phy_my_0.083deg-3D_P1Y-m_202211"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my-aflux_0.027deg_P1H-i_202411"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg-climatology_P1M-m_202311"}, "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411": {"collection": "EO:MO:DAT:IBI_MULTIYEAR_WAV_005_006:cmems_mod_ibi_wav_my_0.027deg_PT1H-i_202411"}, "EO:MO:DAT:INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032:cmems_obs-ins_bal_phybgcwav_mynrt_na_irr_202311": {"collection": "EO:MO:DAT:INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032:cmems_obs-ins_bal_phybgcwav_mynrt_na_irr_202311"}, "EO:MO:DAT:INSITU_GLO_BGC_DISCRETE_MY_013_046:cmems_obs-ins_glo_bgc-nut_my_na_irr_202411": {"collection": "EO:MO:DAT:INSITU_GLO_BGC_DISCRETE_MY_013_046:cmems_obs-ins_glo_bgc-nut_my_na_irr_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_PT1H_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_PT1H_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_irr_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_glo_phy-ssh_my_na_irr_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_ibi_phy-ssh_my_tide-surge_PT1H_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053:cmems_obs-ins_ibi_phy-ssh_my_tide-surge_PT1H_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_MY_013_052:cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_MY_013_052:cmems_obs-ins_glo_phy-temp-sal_my_cora-oa_P1M_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_NRT_013_002:cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_TS_OA_NRT_013_002:cmems_obs-ins_glo_phy-temp-sal_nrt_oa_P1M_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_adcp_irr_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_adcp_irr_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_argo_irr_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_argo_irr_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_drifter_PT1H_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_drifter_PT1H_202411"}, "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_glider_irr_202411": {"collection": "EO:MO:DAT:INSITU_GLO_PHY_UV_DISCRETE_MY_013_044:cmems_obs-ins_glo_phy-cur_my_glider_irr_202411"}, "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_PT1H_202411": {"collection": "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_PT1H_202411"}, "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_irr_202411": {"collection": "EO:MO:DAT:INSITU_GLO_WAV_DISCRETE_MY_013_045:cmems_obs-ins_glo_wav_my_na_irr_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-bio_anfc_4.2km_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1D-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-car_anfc_4.2km_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1D-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-co2_anfc_4.2km_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-nut_anfc_4.2km_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1D-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-optics_anfc_4.2km_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202311": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1D-m_202311"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202311": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_BGC_006_014:cmems_mod_med_bgc-pft_anfc_4.2km_P1M-m_202311"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km-3D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_4.2km_PT15M-i_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-cur_anfc_detided_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-mld_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km-3D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-sal_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_4.2km_PT15M-i_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-ssh_anfc_detided_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-2D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km-3D_PT1H-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-tem_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_PHY_006_013:cmems_mod_med_phy-wcur_anfc_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_WAV_006_017:cmems_mod_med_wav_anfc_4.2km_PT1H-i_202311": {"collection": "EO:MO:DAT:MEDSEA_ANALYSISFORECAST_WAV_006_017:cmems_mod_med_wav_anfc_4.2km_PT1H-i_202311"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-bio_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-car_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-co2_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-nut_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-pft_myint_4.2km_P1M-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:cmems_mod_med_bgc-plankton_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-bio-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-car-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-co2-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-nut-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-d_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_BGC_006_008:med-ogs-pft-rean-m_202105"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-cur_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-hflux_my_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mflux_my_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-mld_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-sal_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-ssh_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-tem_my_4.2km_P1Y-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1D-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy-wflux_my_4.2km_P1M-m_202411"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:cmems_mod_med_phy_my_4.2km-climatology_P1M-m_202211"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-h_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-h_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-cur-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-mld-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-sal-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-h_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-ssh-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-int-m_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-d_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_PHY_006_004:med-cmcc-tem-rean-m_202012"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_my_4.2km-climatology_P1M-m_202311": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_my_4.2km-climatology_P1M-m_202311"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:cmems_mod_med_wav_myint_4.2km_PT1H-i_202112"}, "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411": {"collection": "EO:MO:DAT:MEDSEA_MULTIYEAR_WAV_006_012:med-hcmr-wav-rean-h_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg-climatology_P1M-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg-climatology_P1M-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_BGC_3D_REP_015_010:cmems_obs-mob_glo_bgc-chl-poc_my_0.25deg_P7D-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_my_irr-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_BIO_CARBON_SURFACE_MYNRT_015_008:cmems_obs-mob_glo_bgc-car_nrt_irr-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1D-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_P1M-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_my_0.25deg_PT1H-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1D-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_P1M-m_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_MYNRT_015_003:cmems_obs-mob_glo_phy-cur_nrt_0.25deg_PT1H-i_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-asc_P1D_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-asc_P1D_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014:cmems_obs-mob_glo_phy-sss_mynrt_smos-des_P1D_202411"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L4_MY_015_015:cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W_202406": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_SSS_L4_MY_015_015:cmems_obs-mob_glo_phy-sss_my_multi-oi_P1W_202406"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1D_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1M_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_my_multi_P1M_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1D_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013:cmems_obs-mob_glo_phy-sss_nrt_multi_P1M_202311"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-monthly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-weekly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-nrt-weekly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-monthly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012:dataset-armor-3d-rep-weekly_202012"}, "EO:MO:DAT:MULTIOBS_GLO_PHY_W_3D_REP_015_007:cmems_obs-mob_glo_phy-cur_my_0.25deg_P7D-i_202411": {"collection": "EO:MO:DAT:MULTIOBS_GLO_PHY_W_3D_REP_015_007:cmems_obs-mob_glo_phy-cur_my_0.25deg_P7D-i_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc-optics_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_BGC_004_002:cmems_mod_nws_bgc_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-cur_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-ssh_anfc_detided-0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy-wcur_anfc_0.027deg_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT15M-i_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-2D_PT1H-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1D-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_P1M-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_PHY_004_013:cmems_mod_nws_phy_anfc_0.027deg-3D_PT1H-m_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.027deg_PT1H-i_202411"}, "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.05deg_PT1H-i_202309": {"collection": "EO:MO:DAT:NWSHELF_ANALYSISFORECAST_WAV_004_014:cmems_mod_nws_wav_anfc_0.05deg_PT1H-i_202309"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-chl_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-kd_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-no3_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-o2_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-diato_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-dino_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-nano_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_my_7km-3D-pico_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-diato_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-dino_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-nano_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pft_myint_7km-3D-pico_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-ph_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-phyc_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-po4_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-pp_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_BGC_004_011:cmems_mod_nws_bgc-spco2_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-bottomt_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-mld_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-s_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-ssh_myint_7km-2D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sss_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-sst_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-t_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-2D_PT1H-i_202112"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1D-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_my_7km-3D_P1M-m_202012"}, "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105": {"collection": "EO:MO:DAT:NWSHELF_MULTIYEAR_PHY_004_009:cmems_mod_nws_phy-uv_myint_7km-3D_P1M-m_202105"}, "EO:MO:DAT:NWSHELF_REANALYSIS_WAV_004_015:MetO-NWS-WAV-RAN_202007": {"collection": "EO:MO:DAT:NWSHELF_REANALYSIS_WAV_004_015:MetO-NWS-WAV-RAN_202007"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-plankton_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-reflectance_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L3_MY_009_123:cmems_obs-oc_arc_bgc-transp_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L4_MY_009_124:cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ARC_BGC_L4_MY_009_124:cmems_obs-oc_arc_bgc-plankton_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-optics_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-plankton_my_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-reflectance_my_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_MY_009_113:cmems_obs-oc_atl_bgc-transp_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-optics_nrt_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-plankton_nrt_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-reflectance_nrt_l3-olci-300m_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L3_NRT_009_111:cmems_obs-oc_atl_bgc-transp_nrt_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-plankton_my_l4-multi-1km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_MY_009_118:cmems_obs-oc_atl_bgc-pp_my_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-plankton_nrt_l4-multi-1km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_ATL_BGC_L4_NRT_009_116:cmems_obs-oc_atl_bgc-pp_nrt_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208:cmems_obs_oc_bal_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-optics_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-reflectance_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_MY_009_133:cmems_obs-oc_bal_bgc-transp_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-optics_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-plankton_nrt_l3-olci-300m_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-reflectance_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L3_NRT_009_131:cmems_obs-oc_bal_bgc-transp_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-multi-1km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_MY_009_134:cmems_obs-oc_bal_bgc-pp_my_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_NRT_009_132:cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BAL_BGC_L4_NRT_009_132:cmems_obs-oc_bal_bgc-plankton_nrt_l4-olci-300m_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212:cmems_obs_oc_blk_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-optics_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-reflectance_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_MY_009_153:cmems_obs-oc_blk_bgc-transp_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-optics_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-multi-1km_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-plankton_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-reflectance_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L3_NRT_009_151:cmems_obs-oc_blk_bgc-transp_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_MY_009_154:cmems_obs-oc_blk_bgc-pp_my_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-plankton_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-pp_nrt_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_BLK_BGC_L4_NRT_009_152:cmems_obs-oc_blk_bgc-transp_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-optics_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-plankton_my_l3-olci-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-reflectance_my_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_103:cmems_obs-oc_glo_bgc-transp_my_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-plankton_my_l3-multi-4km_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202303": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_MY_009_107:c3s_obs-oc_glo_bgc-reflectance_my_l3-multi-4km_P1D_202303"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-optics_nrt_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-plankton_nrt_l3-olci-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-reflectance_nrt_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L3_NRT_009_101:cmems_obs-oc_glo_bgc-transp_nrt_l3-olci-4km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-optics_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-multi-climatology-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-plankton_my_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-pp_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-reflectance_my_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_104:cmems_obs-oc_glo_bgc-transp_my_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_108:c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_MY_009_108:c3s_obs-oc_glo_bgc-plankton_my_l4-multi-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-optics_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-plankton_nrt_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-pp_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-reflectance_nrt_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-gapfree-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_GLO_BGC_L4_NRT_009_102:cmems_obs-oc_glo_bgc-transp_nrt_l4-olci-4km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210:cmems_obs_oc_ibi_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211:cmems_obs_oc_med_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-optics_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-multi-1km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-plankton_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-reflectance_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_MY_009_143:cmems_obs-oc_med_bgc-transp_my_l3-olci-300m_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-optics_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-multi-1km_P1D_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-plankton_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-reflectance_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L3_NRT_009_141:cmems_obs-oc_med_bgc-transp_nrt_l3-olci-300m_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-gapfree-multi-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-1km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-multi-climatology-1km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M_202211": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-plankton_my_l4-olci-300m_P1M_202211"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1D_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_MY_009_144:cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M_202311"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-gapfree-multi-1km_P1D_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-plankton_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1D_202411"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-pp_nrt_l4-multi-4km_P1M_202411"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-multi-1km_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M_202207": {"collection": "EO:MO:DAT:OCEANCOLOUR_MED_BGC_L4_NRT_009_142:cmems_obs-oc_med_bgc-transp_nrt_l4-olci-300m_P1M_202207"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l3-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107": {"collection": "EO:MO:DAT:OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209:cmems_obs_oc_nws_bgc_tur-spm-chl_nrt_l4-hr-mosaic_P1D-m_202107"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P30D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat-ssmi-merged_P3D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P2D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_phy_my_drift-cfosat_P3D_202411"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P2D_202311": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P2D_202311"}, "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311": {"collection": "EO:MO:DAT:SEAICE_ANT_PHY_L3_MY_011_018:cmems_obs-si_ant_physic_my_drift-amsr_P3D_202311"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021:cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m_202211": {"collection": "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021:cmems_obs-si_arc_phy_my_L3S-DMIOI_P1D-m_202211"}, "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016:cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m_202105": {"collection": "EO:MO:DAT:SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016:cmems_obs_si_arc_phy_my_L4-DMIOI_P1D-m_202105"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_30DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_ASCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_3DAYS_DRIFT_QUICKSCAT_SSMI_MERGED_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_ASCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:CERSAT-GLO-SEAICE_6DAYS_DRIFT_QUICKSCAT_RAN-OBS_FULL_TIME_SERIE_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P30D_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy-drift_my_l3-ssmi_P3D_202311"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P30D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat-ssmi-merged_P3D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P3D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010:cmems_obs-si_arc_phy_my_drift-cfosat_P6D_202411"}, "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008:DMI-ARC-SEAICE_TEMP-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008:DMI-ARC-SEAICE_TEMP-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m_202211": {"collection": "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_phy-sit_my_l4-1km_P1D-m_202211"}, "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112": {"collection": "EO:MO:DAT:SEAICE_BAL_PHY_L4_MY_011_019:cmems_obs-si_bal_seaice-conc_my_1km_202112"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_CONC-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_CONC-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS": {"collection": "EO:MO:DAT:SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004:FMI-BAL-SEAICE_THICK-L4-NRT-OBS"}, "EO:MO:DAT:SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013:c3s_obs-si_glo_phy_my_nh-l3_P1M_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_PHY_CLIMATE_L3_MY_011_013:c3s_obs-si_glo_phy_my_nh-l3_P1M_202411"}, "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_PHY_L4_NRT_011_014:esa_obs-si_arc_phy-sit_nrt_l4-multi_P1D-m_202411"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_nh_P1D-m_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_nh_P1D-m_202411"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001:osisaf_obs-si_glo_phy-sidrift_nrt_sh_P1D-m_202411"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS_202003": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-NH-LA-OBS_202003"}, "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003": {"collection": "EO:MO:DAT:SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009:OSISAF-GLO-SEAICE_CONC_CONT_TIMESERIES-SH-LA-OBS_202003"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_al-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_al-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_alg-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_alg-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_c2n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1g-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e1g-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_e2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_en-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_en-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_enn-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_enn-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_g2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_g2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2a-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2a-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2ag-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2ag-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2b-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_h2b-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1g-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1g-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j1n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2g-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2g-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j2n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_j3n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3a-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3a-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3b-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s3b-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swon-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swon-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swonc-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_swonc-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tp-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tp-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tpn-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L3_MY_008_061:cmems_obs-sl_eur_phy-ssh_my_tpn-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_MY_008_068:cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.0625deg_P1M-m_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.0625deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.0625deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202311": {"collection": "EO:MO:DAT:SEALEVEL_EUR_PHY_L4_NRT_008_060:cmems_obs-sl_eur_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202311"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057:c3s_obs-sl_glo_phy-ssh_my_twosat-l4-duacs-0.25deg_P1M-m_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_al-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_al-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_alg-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_alg-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_c2n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1g-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e1g-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_e2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_en-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_en-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_enn-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_enn-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_g2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_g2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2a-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2a-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2ag-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2ag-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2b-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_h2b-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j1n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2g-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2g-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j2n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3n-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_j3n-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3a-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3a-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3b-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s3b-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_s6a-lr-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swon-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swon-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swonc-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_swonc-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tp-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tp-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tpn-l3-duacs_PT1S_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L3_MY_008_062:cmems_obs-sl_glo_phy-ssh_my_tpn-l3-duacs_PT1S_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_MY_008_047:cmems_obs-sl_glo_phy-ssh_my_allsat-l4-duacs-0.125deg_P1M-m_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202411": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.125deg_P1D_202411"}, "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D_202311": {"collection": "EO:MO:DAT:SEALEVEL_GLO_PHY_L4_NRT_008_046:cmems_obs-sl_glo_phy-ssh_nrt_allsat-l4-duacs-0.25deg_P1D_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_MY_010_038:cmems_obs-sst_atl_phy_my_l3s_P1D-m_202411": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_MY_010_038:cmems_obs-sst_atl_phy_my_l3s_P1D-m_202411"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_gir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_gir_P1D-m_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pir_P1D-m_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_l3s_pmw_P1D-m_202311"}, "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211": {"collection": "EO:MO:DAT:SST_ATL_PHY_L3S_NRT_010_037:cmems_obs-sst_atl_phy_nrt_l3s_P1D-m_202211"}, "EO:MO:DAT:SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025:IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE_201904": {"collection": "EO:MO:DAT:SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025:IFREMER-ATL-SST-L4-NRT-OBS_FULL_TIME_SERIE_201904"}, "EO:MO:DAT:SST_ATL_SST_L4_REP_OBSERVATIONS_010_026:cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE_202411": {"collection": "EO:MO:DAT:SST_ATL_SST_L4_REP_OBSERVATIONS_010_026:cmems-IFREMER-ATL-SST-L4-REP-OBS_FULL_TIME_SERIE_202411"}, "EO:MO:DAT:SST_BAL_PHY_L3S_MY_010_040:cmems_obs-sst_bal_phy_my_l3s_P1D-m_202211": {"collection": "EO:MO:DAT:SST_BAL_PHY_L3S_MY_010_040:cmems_obs-sst_bal_phy_my_l3s_P1D-m_202211"}, "EO:MO:DAT:SST_BAL_PHY_SUBSKIN_L4_NRT_010_034:cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m_202211": {"collection": "EO:MO:DAT:SST_BAL_PHY_SUBSKIN_L4_NRT_010_034:cmems_obs-sst_bal_phy-subskin_nrt_l4_PT1H-m_202211"}, "EO:MO:DAT:SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032:DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE_201904": {"collection": "EO:MO:DAT:SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032:DMI-BALTIC-SST-L3S-NRT-OBS_FULL_TIME_SERIE_201904"}, "EO:MO:DAT:SST_BAL_SST_L4_REP_OBSERVATIONS_010_016:DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016_202012": {"collection": "EO:MO:DAT:SST_BAL_SST_L4_REP_OBSERVATIONS_010_016:DMI_BAL_SST_L4_REP_OBSERVATIONS_010_016_202012"}, "EO:MO:DAT:SST_BS_PHY_L3S_MY_010_041:cmems_obs-sst_bs_phy_my_l3s_P1D-m_202411": {"collection": "EO:MO:DAT:SST_BS_PHY_L3S_MY_010_041:cmems_obs-sst_bs_phy_my_l3s_P1D-m_202411"}, "EO:MO:DAT:SST_BS_PHY_SUBSKIN_L4_NRT_010_035:cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"collection": "EO:MO:DAT:SST_BS_PHY_SUBSKIN_L4_NRT_010_035:cmems_obs-sst_blk_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_a_202311"}, "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013:SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013_b_202311"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_b"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SSTA_L4_NRT_OBSERVATIONS_010_006_d"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_a_V2_202311"}, "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311": {"collection": "EO:MO:DAT:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006:SST_BS_SST_L4_NRT_OBSERVATIONS_010_006_c_V2_202311"}, "EO:MO:DAT:SST_BS_SST_L4_REP_OBSERVATIONS_010_022:cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022_202411": {"collection": "EO:MO:DAT:SST_BS_SST_L4_REP_OBSERVATIONS_010_022:cmems_SST_BS_SST_L4_REP_OBSERVATIONS_010_022_202411"}, "EO:MO:DAT:SST_GLO_PHY_L3S_MY_010_039:cmems_obs-sst_glo_phy_my_l3s_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_PHY_L3S_MY_010_039:cmems_obs-sst_glo_phy_my_l3s_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_PHY_L4_NRT_010_043:cmems_obs-sst_glo_phy_nrt_l4_P1D-m_202303": {"collection": "EO:MO:DAT:SST_GLO_PHY_L4_NRT_010_043:cmems_obs-sst_glo_phy_nrt_l4_P1D-m_202303"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE_202211": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:IFREMER-GLOB-SST-L3-NRT-OBS_FULL_TIME_SERIE_202211"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_gir_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pir_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311": {"collection": "EO:MO:DAT:SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010:cmems_obs-sst_glo_phy_l3s_pmw_P1D-m_202311"}, "EO:MO:DAT:SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001:METOFFICE-GLO-SST-L4-NRT-OBS-SST-V2"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:METOFFICE-GLO-SST-L4-REP-OBS-SST_202003": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_011:METOFFICE-GLO-SST-L4-REP-OBS-SST_202003"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:C3S-GLO-SST-L4-REP-OBS-SST_202211": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:C3S-GLO-SST-L4-REP-OBS-SST_202211"}, "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211": {"collection": "EO:MO:DAT:SST_GLO_SST_L4_REP_OBSERVATIONS_010_024:ESACCI-GLO-SST-L4-REP-OBS-SST_202211"}, "EO:MO:DAT:SST_MED_PHY_L3S_MY_010_042:cmems_obs-sst_med_phy_my_l3s_P1D-m_202411": {"collection": "EO:MO:DAT:SST_MED_PHY_L3S_MY_010_042:cmems_obs-sst_med_phy_my_l3s_P1D-m_202411"}, "EO:MO:DAT:SST_MED_PHY_SUBSKIN_L4_NRT_010_036:cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105": {"collection": "EO:MO:DAT:SST_MED_PHY_SUBSKIN_L4_NRT_010_036:cmems_obs-sst_med_phy-sst_nrt_diurnal-oi-0.0625deg_PT1H-m_202105"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_a_202311"}, "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012:SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012_b_202311"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_b"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SSTA_L4_NRT_OBSERVATIONS_010_004_d"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_a_V2_202311"}, "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2_202311": {"collection": "EO:MO:DAT:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004:SST_MED_SST_L4_NRT_OBSERVATIONS_010_004_c_V2_202311"}, "EO:MO:DAT:SST_MED_SST_L4_REP_OBSERVATIONS_010_021:cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021_202411": {"collection": "EO:MO:DAT:SST_MED_SST_L4_REP_OBSERVATIONS_010_021:cmems_SST_MED_SST_L4_REP_OBSERVATIONS_010_021_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SPC_L4_NRT_014_004:cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H_202112": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SPC_L4_NRT_014_004:cmems_obs-wave_glo_phy-spc_nrt_multi-l4-1deg_PT3H_202112"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-0.5deg_P1D-i_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-0.5deg_P1D-i_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_MY_014_007:cmems_obs-wave_glo_phy-swh_my_multi-l4-2deg_P1D-m_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-i_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D-m_202411"}, "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D_202211": {"collection": "EO:MO:DAT:WAVE_GLO_PHY_SWH_L4_NRT_014_003:cmems_obs-wave_glo_phy-swh_nrt_multi-l4-2deg_P1D_202211"}, "EO:MO:DAT:WIND_GLO_PHY_CLIMATE_L4_MY_012_003:cmems_obs-wind_glo_phy_my_l4_P1M_202411": {"collection": "EO:MO:DAT:WIND_GLO_PHY_CLIMATE_L4_MY_012_003:cmems_obs-wind_glo_phy_my_l4_P1M_202411"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers1-scat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-ers2-scat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopa-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-metopb-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-oceansat2-oscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_MY_012_005:cmems_obs-wind_glo_phy_my_l3-quikscat-seawinds-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2b-hscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2c-hscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-hy2d-hscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopa-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopb-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.125deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-metopc-ascat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.25deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-asc-0.5deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.25deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-oceansat3-oscat-des-0.5deg_P1D-i_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-asc-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.25deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L3_NRT_012_002:cmems_obs-wind_glo_phy_nrt_l3-scatsat1-oscat-des-0.5deg_P1D-i_202311"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.125deg_PT1H_202211"}, "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H_202406": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L4_MY_012_006:cmems_obs-wind_glo_phy_my_l4_0.25deg_PT1H_202406"}, "EO:MO:DAT:WIND_GLO_PHY_L4_NRT_012_004:cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H_202207": {"collection": "EO:MO:DAT:WIND_GLO_PHY_L4_NRT_012_004:cmems_obs-wind_glo_phy_nrt_l4_0.125deg_PT1H_202207"}}}}